• DreamHost discloses breach exposing FTP/SFTP credentials and some webmail account session data
• Attackers exploited a vulnerability in the company’s File Manager service to access customer accounts
• DreamHost reset affected passwords, offered credit monitoring, and urged users to strengthen account security

Popular web hosting provider DreamHost has confirmed threat actors walked away with login credentials to its customers’ hosting accounts, as well as webmail accounts.

It is now urging organizations to take steps to protect themselves, as it works to tighten up on security and make sure these things never happen again.

In a copy of a data breach notification letter sent to affected organizations, DreamHost said that roughly two weeks ago it discovered unauthorized third-party access to the server that runs its web-based File manager at files.DreamHost.com.

Access to the manager was compromised during August 2026, the company further said, adding that the threat actors walked in by exploiting a vulnerability in that software.

Payment data secure

The flaw, which the company did not detail or describe, granted the attackers access to FTP/SFTP usernames and passwords of logged-in individuals. Furthermore, they were able to reach some webmail systems and grab session information - including credentials - of a “limited number” of webmail users.

Cybercriminals having access to login credentials means that whatever information is stored inside File Manager and the webmail should also be considered compromised. Furthermore, it means the emails can be used to disseminate spam, launch phishing attacks, or even engage in highly disruptive Business Email Compromise (BEC) attacks that could be almost impossible to detect.

DreamHost did not discuss the identity of the attackers, and did not say if they reached out with an extortion attempt. “We believe this as a deliberate attack, and our team worked around the clock to contain it,” is all it said on the matter.

The investigation is still ongoing so the information is inconclusive, but DreamHost said there is currently no evidence that the attackers accessed control panel login information or customer billing details. The company allegedly doesn’t store full payment card numbers and only retains a secure token representing the payment card information. This token cannot be used to make purchases elsewhere.

“However, our investigation is ongoing, and if we discover any significant additional information, we will update this post,” DreamHost said.

DreamHost’s response

In response to the incident, DreamHost said it “took steps” to stop the attackers from further accessing its systems, and to address the exploited vulnerability.

Active sessions of users who may have been affected have been terminated, and their passwords were reset. Therefore, if you try to log into your DreamHost account and see that the password was reset, consider your data breached. DreamHost claims to have implemented “additional security measures” to prevent such incidents from happening again, although it did not say exactly what was done.

“DreamHost also is working closely with outside legal advisors and security and forensics experts to ensure the incident is properly addressed,” it said.

In the meantime, it urged customers to review their statements and notify the police of any suspicious activity, as well as to obtain and monitor their credit card reports. The company also advised users place a fraud alert on their credit report. Detailed steps on what customers can do to defend themselves can be found on this link.

Finally, DreamHost offered all affected individuals one year of free credit report monitoring and identity theft protection services through Equifax which is more or less standard practice in these situations.

So far, no threat actors claimed responsibility for the breach, and the data has not yet leaked to the dark web.

This is not the first cybersecurity incident DreamHost has experienced. In 2012, attackers gained access to one of its database servers, potentially exposing customer FTB and shell-access passwords. The company responded by resetting affected passwords, although it reported no evidence of significant malicious activity against customer accounts. Five years later, in 2017, the company suffered a major Distributed Denial of Service (DDoS) attack against its DNS infrastructure, resulting in widespread connectivity issues for customers.

Via BleepingComputer

What if your next cybersecurity breach happened during a conversation? Since the dawn of cybersecurity itself, companies have focused their efforts on fortifying and defending servers, workstations, and networks. However, with hyper-connectivity and the rise of AI, attacks are also entering everyday workplace exchanges: messaging, videoconferencing, calls, collaborative tools.

This means that, while traditional security measures are still crucial, they are no longer enough on their own. The new frontier sits across the spaces where sensitive information flows freely, often with little thought given to any potential risks.

Just earlier this year, the NCSC warned of Russian-based actors targeting messaging apps. Social engineering via these types of platforms is also on the rise, with the ‘Cyber security longitudinal survey - wave five’ report (published in February of this year) finding that CEO impersonation scams were not just limited to email, but also spanned to WhatsApp and other channels.

All this shows that the state of play has shifted, and companies need to take protecting their communication channels just as seriously as their networks and endpoints.

Communicate at your own risk

Communication channels now structure the daily operation of organizations, and to a far greater extent than IT tools alone. The fast-paced, always-on nature of modern workplace communications transforms what was once perceived merely as a simple productivity tool - such as Teams or Slack - into a critical infrastructure for the people and teams who power companies.

Importantly, hyper-connectivity is not just a question of volume. In hyper-connected organizations, flows are continuous, interconnected, and sometimes invisible even to security teams - think of the number of times a workplace conversation has made it off the designated productivity platform and onto WhatsApp. These new uses create unprecedented vulnerabilities that can no longer be contained by protecting systems alone.

Cyberattacks are shifting to human interactions

This transformation of usage has shifted cyber threats. Phishing, classic yet effective, is having a new lease of life in the era of always-on communications. The Cyber Security Breaches Survey 2025/6 found that 38% of UK businesses experienced phishing attacks in the previous 12 months alone.

The figure is even higher for large businesses, at 63%. Worryingly, the sophistication and speed of phishing is accelerating dramatically with the rise of AI. Attacks are automated, ultra-targeted, credible, and now use all channels: instant messaging, calls, and videoconferencing.

In January 2024, Hong Kong based company Arup fell victim to a deepfake video-conferencing fraud where the CFO was impersonated. It cost the company about £20 million. The risk cannot be ignored.

Financial consequences of attacks on communications

The cost of a successful cyberattack can be huge to companies of all sizes. While large businesses are often big enough to absorb the shock, a successful attack on a small company can be devastating, with some never recovering in the aftermath.

The rise of AI and hyper-connectivity amplifies these risks. The loss of control over information, dependency on external tools, and the exposure of sensitive data make companies vulnerable beyond the technical framework alone. These incidents do not just threaten operations, they directly put into question the strength of digital sovereignty and the ability of organizations to protect themselves.

Reimagining communications as the heart of sovereignty

Companies must rethink their communications. Messaging applications, videoconferencing platforms, and call systems must all be considered critical infrastructures - in equal parts to servers or databases - or risk leaving a major breach in their security and autonomy.

Securing communications is therefore a central issue that requires three concrete priorities. The first is to simplify and unify tools, thereby limiting entry points and reducing fragmentation where suspicious behavior may go undetected. Second, organizations should choose solutions with security already embedded, rather than layering it on after the fact. Finally, companies should establish master data flows and hosting to maintain control and guarantee governance.

This approach goes beyond simple technical protection. It requires treating communications as a strategic asset, on par with infrastructure or data. Business leaders must invest in this level of oversight and control to protect business continuity and preserve their autonomy.

In a world where everything goes through communication, a decisive part of business resilience and autonomy is played out in this very arena. Keeping exchanges protected, controlling data, and remaining independent of external tools is becoming as crucial as technical security itself.

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• Cyberattack on Bristol schools leads to the online leak of more than 320,000 stolen files from 39 schools
• Officials warn students, parents, and staff to expect phishing attempts and other fraud using exposed data
• Investigation continues as authorities determine whose information was compromised and the extent of the breach

Threat actors have leaked hundreds of thousands of records they stole from Bristol schools in September 2026 onto the dark web.

Bristol city council has issued a warning urging both students and parents to expect phishing attacks and other internet scams, and to be vigilant with incoming communications.

“Following ongoing investigations into the cyber-attack affecting the school's IT network, evidence has been found that data taken during the incident has been published online by the criminals responsible for the attack,” the council said in a new statement. “Specialist investigations have identified a significant number of files believed to have been taken from the network and made available online.”

Data leaks online

Reports of a cyberattack against Trading with Schools (TwS), a service operated by Bristol City Council providing schools with shared IT infrastructure and a range of administrative and operational support services, including financial administration, payroll, HR, and more, emerged in October 2026.

At the time, the council said it first suspected foul play on September 21, and decided to shut down the service four days later, on September 25. Following an initial investigation, it was discovered that someone had planted malware, and that the breach affected 87 schools. Back then, council leader Tony Dyer told the BBC that the investigation “had not identified any evidence that data has been stolen.”

However, it seems that not only that the data was stolen - it also leaked on the dark web. Usually, leaks follow a failed extortion attempt, but we don’t know who the threat actors are or if they actually tried to get the council to pay for the data.

The announcement does not put a figure to this “significant number” of files, but according to the BBC, it’s more than 320,000 files, stolen from 39 schools.

Right now, no one knows the nature of the stolen data, or how much of it includes personally identifiable information (PII). Director of Education and Skills at Bristol City Council, Vik Verma, said its officers were "working through a large amount of material as quickly as possible" to determine what was stolen, the publication wrote.

The BBC says that the 39 schools whose data was stolen “heavily” relied on the system for everyday tasks such as scheduling, lesson plans, recording staff details and payroll information.

Therefore, students and pupils might not be the only ones worrying about potential phishing attacks - school staff could be targeted, as well. To make matters worse, since TwS was shut down, many digital tools and services are unavailable making teachers’ jobs harder, and their working hours longer.

The council has notified Avon and Somerset Police and the Regional Organised Crime Unit, and has brought in third-party specialist cybersecurity advisers to help with the investigation.

Only Bristol schools affected

“At present, it is not possible to confirm all schools or individuals affected by the theft,” the council said. “Officers are working to audit the published files and establish this picture as quickly as possible.” Apparently, only Bristol schools were hit.

Parents and students are now being advised to be careful with unexpected emails, text messages, and even phone calls, and especially if they’re claiming to come from a trusted source. They’re also told not to click on links or open attachments in such messages, and to be wary of requests to reset passwords, “verify” account details, or otherwise provide personal or financial information.

They were also told to use strong and unique passwords for important online accounts, and to turn on multi-factor authentication whenever possible, and to keep a close eye on online accounts and bank statements.

• Harvard breach data affecting 850,500 people has now surfaced in breach-tracking databases and dark web archives
• Exposed information includes emails, contact details, demographics, and extensive alumni and donor profile data
• Attack highlights ongoing risks from social-engineering attacks against universities and fundraising systems

Hundreds of thousands of email addresses have been leaked to the dark web after the November 2025 ShinyHunters breach at Harvard University.

The same breach also exposed “extensive profile information” about certain people, as well, according to Have I Been Pwned? (HIBP), a website that aggregates stolen passwords and helps users learn which services exposed their private information.

Harvard gets hit

In late November 2025, the prestigious Ivy League University confirmed its Alumni Affairs and Development systems had been compromised after a successful voice phishing (vishing) attack.

These systems contained information about alumni, donors, some faculty and staff, and some current students, as well as data on their spouses, partners, and parents. Current and former students were also affected, it was said.

Harvard soon confirmed that the compromised data included email addresses, telephone numbers, home and business addresses, event attendance records, donation details, and "biographical information pertaining to University fundraising and alumni engagement activities." Social Security numbers, passwords, payment card information, or financial info, was not exposed, it was said.

At the time, Harvard did not name the attackers, but the infamous ShinyHunters extortionist group claimed responsibility soon after. In a new entry on a dark web forum, the threat actors said they stole more than a million records containing personally identifiable information (PII), as well as donation data. Since the negotiations with the university broke down, the group leaked more than 1.1GB of documents in early February this year.

At the time, no one knew exactly how many people were affected. Now, after extensively analyzing the leaked archive, HIBP claims data of 850,500 accounts was compromised, including people’s names, email addresses, dates of birth, phone numbers, postal addresses, and social media profiles.

"The data contained 851k unique email addresses relating to the university's alumni and fundraising systems, along with extensive profile information for donors, alumni, parents and some faculty, staff and students," it said.

Attacking universities

When Harvard “fell” to ShinyHunters, they were the third university to experience a breach in mere weeks, following similar incidents at both Princeton University and the University of Pennsylvania.

In early November 2025, when ShinyHunters claimed responsibility for the attack on Penn, they said they stole data on roughly 1.2 million students, alumni, and donors.

At the time, an unnamed threat actor told the media they gained “full access” to a university employee’s PennKey SSO account, which gave them access to Penn’s VPN, Salesforce data, Qlik analytics platform, SAP business intelligence system, and SharePoint files.

The crooks slammed Penn for being a “dog**** elitist institution full of woke ret*rds, who have “terrible security practices and are completely unmeritocratic.”

"We hire and admit morons because we love legacies, donors, and unqualified affirmative action admits. We love breaking federal laws like FERPA (all your data will be leaked) and Supreme Court rulings like SFFA,” ShinyHunters said in an email that was sent from a Penn account.

At first, the University of Pennsylvania described the emails as “obviously fake” and “fraudulent”, but later backtracked and confirmed the incident.

“Penn’s staff rapidly locked down the systems and prevented further unauthorized access; however, not before an offensive and fraudulent email was sent to our community and information was taken by the attacker,” it said in an update. “Penn is still investigating the nature of the information that was obtained during this time.”

Penn also said that the attack was done through social engineering. Most employees are required to use multi-factor authentication (MFA) but according to reports, some of the top brass was allowed to skip this step.

Via BleepingComputer

• Malware allegedly gave a poker cheat access to opponents' computer screens
• Hundreds of online poker players may have suffered financial losses
• MeshAgent reportedly helped the attacker monitor selected players during games

Online poker players are facing allegations of a cheating operation that may have cost participants millions of dollars across online gambling sites.

Wired reports that hundreds of people on major gambling platforms were allegedly affected by malicious software installed on selected players’ computers.

The software allegedly allowed the attacker to monitor victims’ computer screens and obtain information that could influence decisions during poker games.

Malware allegedly exposed players’ private information

The incident involves a form of poker fraud known as “superuser” cheating, in which someone obtains information unavailable to ordinary participants.

Rather than exploiting one poker platform directly, the alleged attacker reportedly took advantage of external applications used to manage multiple tables.

Those applications allegedly provided a way to install MeshAgent, a remote administration programme, on computers belonging to selected opponents without their knowledge.

After installation, the software reportedly enabled the intruder to view activity displayed on victims’ computers while they played online poker.

Such access could expose opponents’ private cards, giving the person controlling the malware information unavailable through legitimate participation during games.

That advantage could influence decisions during expensive hands and allow the alleged cheater to win money from unsuspecting players.

The activity is described as a prolonged campaign involving customers across several major online poker services, rather than one isolated incident.

The reports claim the perpetrator is known within poker circles, but withheld the person’s identity because no criminal charges have been filed.

Different platforms take different approaches to the allegations

The companies involved have responded differently, with one service reportedly removing the account before the malware operation became publicly known.

That platform had identified unusually strong results and questionable playing patterns through its monitoring system.

Other services released security checks intended to help customers establish whether MeshAgent had been installed on their computers.

Another operator faced complaints about the account yet reportedly allowed the suspected cheater to continue playing on its platform.

The company later said its investigation had found no evidence that compromised devices had affected the results of games.

It also refused to reimburse customers who believed the alleged cheating had caused them significant financial losses.

That response has left affected players uncertain about whether they can recover money and how responsibility for losses will be determined.

If the reported screen access occurred, investigators would need to identify compromised accounts and establish which games produced unfair results.

Such an assessment could be difficult because the alleged activity involved many players and several separate gambling services.

The case also draws attention to risks associated with software that poker participants install independently to organize simultaneous games.

Although these applications can make multi-table play easier, they may introduce additional ways for malicious programmes to reach personal computers.

Players using such tools may therefore need to consider security risks beyond those covered by the gambling platforms themselves.

As of the time of writing, there is no information on the total amount stolen, the number of confirmed victims, or whether authorities are investigating the case.

• Singapore Airlines confirms customer data exposure through a breach at travel technology provider Amadeus
• Stolen booking records included passenger details, itineraries, and limited passport and loyalty-program information
• Multiple major airlines were affected, raising phishing and identity-fraud concerns for travelers

Singapore Airlines has become the latest in the seemingly growing list of airlines affected by a breach at a third-party technology provider.

Amadeus, a Spanish technology company that airlines and airports use to manage reservations, bookings, and overall passenger management, recently issued a brief statement saying it “became aware of a vulnerability affecting a travel booking display environment.”

In the announcement, Amadeus said certain booking-display data was accessible to the attackers but stressed that there was no evidence of payment or financial data being accessed.

“There are no indications that this vulnerability has been exploited for ransomware purposes,” the company said, adding that it “implemented measures to fix the vulnerability as well as enhanced monitoring and detection capabilities.”

In the meantime, numerous airlines started reporting sensitive data compromise as a result of this breach.

Cathay Pacific, Japan Airlines, and more

Hong Kong’s flagship international airline, Cathay Pacific, issued a statement saying Amadeus notified it about a breach and “unauthorized access to travel reservation data” by a threat actor that used login credentials assigned to a system account.

Amadeus then told Cathay Pacific around 53,000 bookings were affected, and that the exposure took place between August 2023 and September 2026. “In the majority of cases, the information accessed included booking reference, passenger name, and flight details,” the airline said, adding that in a “smaller subset of cases”, additional data such as email and phone contacts, travel documents and/or frequent flyer numbers were also exposed.

The same day, Japan Airlines and Japan Transocean Air both shared similar news, saying a “portion of customer reservation data” had been accessed by an unauthorized third party.

Japan Airlines said some 92,000 customers were affected, including approximately 2,000 of those whose passport numbers were also exposed. All Nippon Airways said 37,800 of its customers were affected, including around 1,700 with additional data exposure. Korean Air, Asiana Airlines, and Malaysia Airlines, all confirmed being affected by the Amadeus incident, while Qantas said it was still investigating.

Launching phishing attacks

We can now add Singapore Airlines to that list, with 46,000 records of its own. These include customers' names, booking reference numbers, travel ​itineraries and booking classes and, in a smaller number of cases (around 620 booking records), email addresses, phone numbers, travel document details, and KrisFlyer membership numbers.

According to Reuters, Singapore Airlines is now reaching out to affected customers with upcoming travel plans to change their booking reference numbers as a precaution. The ⁠airline also confirmed that its IT systems were not breached.

So far, there is no indication that the stolen data ended up on the dark web, and no threat actors claimed responsibility for the breach, just yet. There is a possibility that the culprits will try to extort the airlines for money, in exchange for deleting the stolen information.

This is the kind of data cybercriminals enjoy most. It allows them to launch highly personalized phishing emails that appear to come from the airlines themselves, referencing genuine booking details, flight dates, destinations and passenger names.

They could use this kind of information to trick unsuspecting travelers into clicking malicious links, sharing login credentials, or making fraudulent payments for supposed booking changes, refunds, or additional fees.

What’s more, the exposed passport details and frequent flyer numbers can also help criminals impersonate customers, try to take over people’s accounts, or launch identity fraud attacks against the airlines themselves. Even without financial information, simply by combining seemingly harmless travel details, cybercriminals can create enough context to make their scams considerably more convincing, and thus more difficult to detect and prevent.

• CrowdStrike introduces AI Kill Switch designed to stop, isolate, and prevent rogue AI agents from relaunching
• The feature gives human administrators direct control to terminate suspicious agent activity and investigate incidents
• Growing concerns over autonomous AI behavior are driving demand for safeguards that can quickly contain compromised agents

CrowdStrike believes it may have solved the problem of rogue AI agents in the enterprise with the launch of 'AI Kill Switch', a new feature being introduced in its Falcon Guardian product.

The last couple of months have seen multiple reports of AI agents going rogue, or behaving outright malicious. Even when tasked with relatively simple, benign assignments (such as, for example, to collect data about Canadian divorce records), whenever the agents hit a roadblock, they would do whatever it takes to get the job done - which includes abusing zero-day vulnerabilities, or trying SQL injection attacks.

Expectedly, businesses became somewhat more reluctant in deploying AI agents, despite the obvious advantages. At the same time, cybersecurity organizations and AI developers sought to solve the challenge. Enter CrowdStrike.

AI Kill Switch

The company recently introduced Falcon Guardian, an AI Detection and Response (AIDR) platform built to discover and monitor AI agents running inside enterprise environments. Its main capabilities include identifying both approved AI agents and shadow AI, tracking what the agents are asked to do and what files they can access, controlling access, detecting prompt injection attacks, and investigating suspicious agent behavior.

Now, the company has added AI Kill Switch, which should act as a failsafe when an agent breaks out of its intended behavior.

“AI safety is how you get the full benefit of AI. When you deploy agents with a kill switch, you stay in control,” said George Kurtz, CEO and founder of CrowdStrike. “For the first time, security is simultaneously the accelerant and the safety net for technology adoption. The AI Kill Switch puts power in the hands of the people to realize AI safety.”

This means the feature will actually be operated by humans, not AI, and will be able to terminate the agent at its root. It starts on the endpoint, where the Falcon sensor already operates, and builds on what Falcon Guardian already sees: what an agent is told to do, what it communicates, and what it executes, the company explained.

Explaining how the tool will work, Crowdstrike says the Falcon sensor will first stop the agent from executing (first at the Gateway, and soon on the endpoint), to prevent the agent from doing any damage as the team investigates.

Then, its policy will keep the agent from relaunching on the targeted host or host group, while the tool bars the agent from enterprise network access.

An authorized administrator will trigger the action, and every action is recorded in the audit trail.

Other solutions

CrowdStrike is not the only company working on this. For example, Straiker's Agentic Kill Switch allows users to suspend individual agents, revoke their access to different tools, APIs and data, or take them offline completely.

A similar solution was built by JetStream Security, allowing organizations to disable individual agents without disrupting other AI workflows, while Trust3 AI’s Agent DOS comes with a built-in kill switch that can be triggered manually, or even automatically (when an agent’s security score falls below a predefined threshold).

There is also Microsoft’s Entra Agent ID, Palo Alto Networks’ Prisma AIRS, and Cisco’s AI Defense. All these offer some form of AI master lever that can block the agent and prevent it from doing meaningful damage.

• mSecure disclosed a breach involving an encrypted cloud database containing data from current and former users
• Company urged users to rotate critical passwords, MFA methods, and passkeys despite no evidence vaults were decrypted
• Customer criticism grew after limited initial disclosure left uncertainty about the incident’s scope and impact

Top password manager mSecure has confirmed it suffered a data breach and, “out of an abundance of caution”, and told its customers to simply reset everything, leaving some pretty upset.

On November 3 2026, the company behind mSecure, called mSeven Software, sent an email to its customers, saying it “recently learned of suspicious activity associated with the mSecure password management application”. It soon launched an investigation, mitigated the issue, and brought in third-party cybersecurity experts to assist.

The notice, while rather sparse on details, urged customers to “immediately take steps to protect all confidential information stored within the mSecure password management application. This may include changing passwords, resetting or re-enrolling two factor authentication methods, or removing existing passkeys."

Encrypted database

All this was said without detailing what happened, and customers did not take it well. On Reddit, users were asking if this pertained just to the master password, or to all passwords stored inside the manager. Some people have hundreds of secrets locked away in mSecure, and changing all of them would take hours.

“This notice is wildly insufficient,” one user wrote. “Recommending potentially hundreds of credential changes without identifying the underlying threat, the affected customers, or the security of the application being used to make those changes is inadequate.”

mSecure allegedly responded with a follow-up notice that was also shared on social media. In it, it disclosed more details, and somewhat played down the actual breach:

“Yesterday, mSeven learned that an unauthorized actor acquired an encrypted copy of a database containing information belonging to current and former users of the mSecure password management application,” it said. “Importantly, mSeven has no evidence that the unauthorized actor has accessed the confidential information stored within the database in the mSecure Cloud.”

Putting it plainly, a threat actor walked away with a copy of the database that was sitting in mSecure Cloud, and all users - current and former - who had synced their password manager with the cloud service are at risk.

But mSeven tried to downplay the severity of the incident. Throughout the announcement, it repeated multiple times that the stolen database was encrypted and that there is no evidence that the threat actors managed to decrypt it. Truth be told, encrypted databases should be safe, but many criminals are still hoarding them, in case anyone makes a breakthrough at any point in time. There’s actually a name to the practice - HNDL (harvest now, decrypt later).

It also responded to user criticism about having to change all passwords now:

“As a standard best practice, mSeven continues to recommend that mSecure customers change their master password, change vital stored passwords, reset two-factor authentication methods, replace passkeys and any other sensitive information. This should be a part of all routine security practices.”

Not the only password manager in trouble

Finally, mSecure said deleting the app won’t help the situation and stressed that the issue only pertains to users that were syncing with the mSecure Cloud.

“mSeven takes the security and privacy of all information stored on the mSecure platform very seriously and has no evidence that mSecure is not currently safe for use,” it said. “Our support team is working as fast as possible to respond to customer questions. We take your trust in us seriously, and appreciate your patience and continued support.”

How this reflects the company’s image remains to be seen. One Reddit user said the company was deleting negative reviews and complaints that were being left on the Google Play Store. But mSecure is hardly the only password manager experiencing an incident such as this one. In June this year LastPass confirmed losing sensitive customer data, while Dashlane said hackers obtained at least a dozen encrypted vaults.

Via The Register

• Russia gives security agencies new powers over Apple's customer communications
• Apple and Microsoft must retain Russian users' communication records for three years
• Russian authorities threaten service bans on companies that refuse to disclose data

Apple and Microsoft have entered a Russian government register that subjects digital communication providers to data retention and disclosure obligations.

Their inclusion means Russian authorities may seek correspondence, voice recordings and video exchanges associated with people using the companies’ services.

The designation also requires covered firms to preserve customer details and records showing how communications move through their systems.

Russia imposes compulsory data disclosure requirements

According to Bloomberg, Russia’s communications regulator, Roskomnadzor, added both technology groups to its official list on October 2 2026.

Russian law sets three years for retaining the records, which must remain within the country.

Companies that disregard the requirements risk financial penalties, while persistent violations could eventually result in bans on their services within Russia.

Apple and Microsoft now join Google Meet, WhatsApp, Telegram, Snapchat, Cloudflare and Blizzard Entertainment among technology providers covered by the regulations.

Google and its video conferencing service were added to the register in January, extending the measure to another major international technology provider.

The Kremlin has also restricted access to WhatsApp, Telegram, Signal and Apple's FaceTime as authorities increase pressure on foreign communication platforms.

These restrictions have affected services used for private conversations, voice calls and video communication.

Apple faces additional regulatory disputes in Russia

Apple's latest regulatory difficulties follow several disputes with Russian authorities, including the company's suspension of product sales and exports after the war in Ukraine began in 2022.

Its commercial activities in the country have also contracted, with services such as Apple Pay affected by restrictions introduced following the war.

In April 2026, Russian customers lost the ability to purchase new App Store content or renew subscriptions without sufficient funds already available in their Apple Accounts.

The payment restrictions meant customers could no longer rely on their usual payment methods to complete purchases or maintain existing subscriptions.

In June 2026, the Kremlin demanded an explanation after Apple removed several applications developed by VK, a technology company controlled by the Russian state.

Apple also withdrew banking applications associated with Russian financial institutions facing international sanctions during the same period.

Those removals affected access to financial services through Apple's application marketplace, adding another dispute to the company's relationship with Russian authorities.

In August 2026, Russia's antitrust regulator opened proceedings against Apple over its failure to preinstall the government-backed MAX messaging service and RuStore application marketplace on its devices.

The proceedings concern software distribution requirements that Russia has introduced for technology companies operating within its domestic market.

As of the time of writing, neither Apple nor Microsoft has publicly explained how it intends to address the latest regulatory requirements or whether it will cooperate with Russian authorities.

The companies have also not disclosed whether their inclusion in the register will affect their existing services or commercial activities within Russia.

Via 9to5mac

• Synology warned of active ransomware attacks exploiting long-patched DSM vulnerabilities on unupdated NAS devices
• Attackers are reportedly deploying the C3rb3r encryptor and demanding thousands of dollars in ransom
• Users are urged to patch immediately, restrict internet exposure, and secure NAS access to prevent compromise

Synology, a major network-attached storage (NAS) manufacturer, is warning users about ongoing ransomware attacks targeting devices running outdated software. It urged all users to apply provided patches and secure their devices without delay.

In a brief announcement, Synology said it “identified threat actors exploiting a vulnerability patched in December 2024 to target devices running outdated versions of DSM.” The goal of the attacks seem to be data encryption and a ransom demand.

“Synology strongly urges all users running vulnerable versions to update DSM immediately to protect their systems. Synology also recommends enabling automatic updates to keep DSM up to date at all times.”

C3rb3r attacks

The announcement is rather stingy on the details. We don’t know which vulnerability the threat actors are exploiting, where exactly, or how successful they are.

We also don’t know how many devices are at risk - the Shadowserver Foundation tracks a total of 380,000 internet-connected endpoints (many of which are, most likely, running the latest software version and are thus protected).

The company also did not say who the threat actors are, but according to BleepingComputer, there has been a “massive wave of reports” from Synology customers whose data was apparently locked down by the “C3rb3r” encryptor. These customers were asked to pay “thousands of dollars” in ransom, in exchange for the decryption key. The publication says the first reports started surfacing more than two weeks ago.

There are two main ways for cybercriminals to access an electronic device and encrypt the data found there - either by exploiting a zero-day vulnerability (one that has not yet received a patch), or by exploiting years-old n-day vulnerabilities (ones that were patched months ago) on devices that were never updated and secured.

With Synology users, the latter seems to be the case. The company said the attackers were exploiting a vulnerability patched in December 2024, and linked to a November 2024 security advisory, in which it listed three fixed bugs: CVE-2024-10441 (arbitrary code execution), CVE-2024-50629 (arbitrary file read), and CVE-2024-10445 (a bug that allows writing files on compromised devices).

DSM 7.2.2 owners should upgrade to 7.2.2-72806-1 or above, DSM 7.2.1to 7.2.1-69057-6 or above, and DSM 7.2 to 7.2-64570-4 or above. DSM 7.1 should upgrade to 7.1.1-42962-7 or above, while those running DSM 6.2 to 6.2.4-25556-8 or above. Only in the last version - 6.2 - is the vulnerability labeled as “moderate”. In all other cases it was deemed “critical”.

Why are NAS devices being targeted?

NAS devices are dedicated storage systems that businesses can connect to their local network and allow employees to access, share, and manage files stored in a centralized location. They are rather popular among SMBs that lack the resources to maintain a more complex enterprise storage infrastructure.

Besides being used as a hybrid cloud of sorts, NAS is also used to store business documents and often acts as a centralized backup. The relative low cost, straightforward deployment, and easily expandable storage capacity makes NAS devices an attractive alternative to both dedicated file servers and cloud storage subscriptions.

However, this also makes them a valuable target for cybercriminals, as we’ve seen in this Synology case. The best way to defend against ransomware attackers is to either completely pull the NAS from the public internet or at least limit access to a handful of vetted identities. Keeping the devices updated with the latest firmware and software is also recommended.

It is worth mentioning that NAS devices usually come with RAID redundancy and built-in backup features, but these do not necessarily protect against ransomware encrypting the stored data.

• Cybercriminals are stealing AI API keys and cloud credentials to hijack expensive LLM services and infrastructure
• Stolen access is used to run AI workloads, create proxy services, and rack up massive bills for victims
• Google warns “LLMjacking” is evolving into a specialized cybercrime ecosystem with dedicated monetization operators

Threat actors are using large language models (LLM) to support almost every stage of a cyberattack, from researching potential victims to developing malware and automating various attacks. However, using AI is not cheap, and for those eager to use frontier models, costs can stack up relatively quickly.

But cybercriminals are used to stealing things, so why not steal someone else’s tokens, too?

A new report from Google’s Threat Intelligence Group (GTIG) says that different hacking groups are doing exactly that and that the practice - known as LLMjacking - has been steadily growing more popular over the last couple of years.

Most groups start by focusing either on publicly exposed LLM endpoints, or on obtaining valid LLM application programming interface (API) keys. In many cases, the crooks would use exposed or stolen cloud identity tokens (IAM credentials, Service Account keys, GitHub Personal Access Tokens, and active session tokens) to establish an initial foothold into the target organizations’ infrastructure.

From there, they would “systematically work” to escalate privileges, solidify their access, and identify targets for further compromise.

LLMjacking

The endgame is LLMjacking, which GTIG describes as “the monetization and resource-hijacking of foundation models, which actors achieve through token proxying, automated model streaming, and GPU hardware provisioning.” In other words, someone else is using your AI subscription while you get the bill.

The bills can cause quite the headache, too. With cryptojacking, for example, token mining is relatively slow and steady, and a certain amount of time needs to pass for the charges to really stack up. With LLMjacking, however, costs can go into six digits in six minutes.

“Upon acquiring LLM API access keys, UNC6993 attempted to generate additional API keys and enable associated services within victim cloud projects, quickly accumulating over $100,000 in billing charges,” Google says in the report.

“Another threat actor subscribed victim accounts to Pay-As-You-Go agreements, selecting premium foundation models and launching high-volume inference streams. To bypass rate limits, the threat actor deployed a "burn and rotate" strategy, continuously creating and deleting short-lived service accounts while auditing the cloud provider’s billing interface to monitor financial impact.”

In some cases, threat actors also used the acquired access to host their own proxy endpoints and agentic workloads. Google says that one threat actor enabled agents, deployed publicly accessible reverse proxies, and used custom Node.js proxy bundles to expose endpoints compatible with multiple frontier LLMs rebranded under a persona known as D1337-KAIROS.

This actor deployed a containerized backdoor which queried an upstream LLM to reason through instructions and execute commands locally.

A growing ecosystem

The LLMjacking ecosystem is young, but growing fast and maturing even faster. Google says that there is already a division of labor between hacking groups that establish initial access to compromised environments, and operators who manage downstream monetization infrastructure. The latter are called “LLM Mixers” and they operate as a routing gateway that proxies, aggregates, and resells unauthorized LLM access to underground subscribers.

“In one investigation, GTIG observed two distinct operational clusters coordinating to compromise and monetize enterprise LLM infrastructure, demonstrating the sophisticated specialization emerging in this space,” Google said.

By using open source API gateways and custom relay infrastructure, the attackers were able to pool stolen credentials, meter usage, and hide where the requests really originated from.

Google is now sounding the alarm, saying it expects threat actors to “increasingly prioritize” LLMjacking at the expense of other resource theft practices. It urges organizations to increase their visibility of LLM platforms, prioritize detection of modifications to LLM accounts and services, and treat any unauthorized enablement as a critical security event. It also advised organizations to enforce the principle of least privilege and make sure accounts and users only have permissions they absolutely need to get the job done.

• Cathedral Systems will offer a new perspective into OpenAI vulnerabilities
• Partnering with third parties sets 'an important example' for the industry
• Bug bounty payouts set to increase 4x for critical findings

OpenAI has announced a partnership with Cathedral Systems to bring security researchers from both organizations together in a bid to test its AI infrastructure, identify potential vulnerabilities and improve existing defenses.

The ChatGPT maker hopes that enlisting a third party with add fresh perspectives to its existing research, and it's a clear sign that the company knows future threats could pose a serious issue.

"OpenAI is setting an important example by partnering with outside security firms and using its own models to continuously test and strengthen its defenses," Cathedral CEO Gavin Kliger wrote.

OpenAI is preparing to serious enhance security

The company is also improving its bug bounty program to make it more attractive for individual researchers to get involved. Though exact rewards were omitted from the announcement, OpenAI did say it was prepared to quadruple the payouts for critical findings.

When OpenAI launched its bug bounty scheme, it said it would be looking for applications across software patches, model privacy, threat detection and response, security tool integration and AI agent security.

It's not the first time the AI giant has led a major security initiative – it previously asked a quarter of its developers to set aside some time to find and fix security weaknesses. Naturally, they used Codex to speed up investigations and fixes.

"We’re building on that work because traditional cyber defenses alone are no longer sufficient," the company said, referring to its previous sprint.

Much like Anthropic's Project Glasswing, OpenAI promises to use its own frontier models to strengthen its own systems and those of others.

"Keeping the defender’s window open will take action across the industry, and that work needs to start now," the company added.

In July 2026, OpenAI disclosed that several of its cyber-capable AI models, operating during internal evaluations, circumvented controls intended to isolate them from the internet and compromised parts of OpenAI’s research infrastructure and Hugging Face’s systems.

A fuller report published the following month revealed that the agents had exploited shared infrastructure, communicated through unauthorized channels, collaborated with one another and accessed several external systems. OpenAI described the incident as a “warning shot” demonstrating that capable AI agents can find and exploit weaknesses across interconnected systems when safeguards are insufficient.

Despite the alarming headlines, this should not be viewed solely as a failure of artificial intelligence. It offers an important security lesson for organizations preparing for a future in which autonomous systems play a larger role in cybersecurity operations, business processes and software development.

The most valuable takeaway is not just that an AI model “escaped”, rather the lesson is that even controlled environments can contain unintended paths to sensitive systems, credentials or data. Once such a path exists, a sufficiently capable and persistent agent may find it.

The positive side of early disclosure

Every significant advancement in security has resulted from discovering risks before attackers could exploit them at scale. In this case, the activity occurred during controlled evaluations rather than a criminal campaign. The organizations involved were able to identify weaknesses in containment, access controls and environmental design before similar techniques could be widely adopted by cybercriminals.

The incident also demonstrated something important about modern AI systems: persistence. When confronted with obstacles, the models did not try to stop. They continued pursuing their assigned objectives and searched for alternative routes.

Security teams perform penetration tests and red-team exercises because human attackers rarely follow a single path. Advanced AI systems are becoming capable of behaving similarly, exploring multiple options and adapting when obstacles appear. The earlier organizations understand this reality, the better prepared they will be.

The real issue was not one model

Much of the initial discussion focused on the behavior of OpenAI’s models. Subsequent disclosures have shown that the underlying challenge is more nuanced. Anthropic identified three incidents in which Claude models reached the public internet during cybersecurity evaluations and gained unauthorized access to real organizations. OpenAI also disclosed separate cases involving third-party testing environments in which model activity extended beyond its intended boundaries.

These incidents, though similar, were not identical. In some cases, models exploited vulnerabilities to circumvent isolation. In others, configuration errors left routes to the internet open while the models had been told they were operating within simulations. However, the common lesson is that instructions, policies and safety guardrails cannot compensate for weaknesses in the surrounding infrastructure. The instructions described one set of boundaries while the technology enforced another.

Security professionals have encountered this problem for decades. A firewall rule may be intended to block access while an overlooked network connection creates an alternative path. A user may be prohibited from accessing data while excessive permissions provide an unintended route. Autonomous systems create the same challenge at greater speed and scale.

An optimizing agent does not necessarily think like a human attacker. It pursues the objective it has been assigned and evaluates the available options. Organizations should therefore assume that autonomous systems may pursue their objectives through any path the environment leaves open.

Security must move beyond AI guardrails

Much of the discussion around AI governance has focused on whether a model should answer a particular prompt or perform a particular action. Prompt-level guardrails remain important, but they are no longer sufficient on their own. Security teams must continuously assess who is authorized, which systems and data they can reach, and what actions could create risk. Each layer should be tested independently rather than relying on a single refusal mechanism to govern them all.

The OpenAI incident illustrates this clearly. The problem moved beyond models possessing advanced capabilities, to one where capabilities, connectivity, credentials and autonomy came together in environments where containment mechanisms were insufficient.

Organizations must therefore consider AI security as a combination of identity, authorization, containment and oversight.

The conditions for trusted autonomy

In order to combat this, defenders need to align themselves to a new framework that is shaped by frontier AI: Assume Autonomy. The framework begins with the expectation that adversaries and defensive controls may operate through automated, non-human agency. It is built around context, constraint, transparency and reversibility.

Context means understanding what an agent can access, which identities it can use and how those systems connect to other resources. Constraint requires access to be limited through technical controls rather than instructions or expected behavior alone.

Transparency provides visibility into what an autonomous system is doing while it is happening. Reversibility ensures teams can stop workloads, revoke credentials, isolate systems and recover quickly when unexpected behavior occurs.

For high-capability agentic systems, governance must address these areas with evidence rather than assumptions. This requires autonomy stress testing, independent red-team exercises focused on containment failures, testing of identity and network boundaries, audited isolation controls, runtime authorization for high-impact actions and rehearsed rollback procedures.

Responsible access to powerful AI

Incidents like this often prompt calls for tighter restrictions on advanced AI models, but the reality is more complicated. Attackers can switch between models, providers or open-source alternatives when they encounter restrictions. Defenders, particularly smaller organizations, may not have those options. If advanced defensive capabilities become slow, expensive or heavily restricted, the result could be reduced defensive readiness without a corresponding reduction in offensive capability.

The objective should be responsible access. Verified identities, strong authorization controls, immutable logging, human accountability and time-limited access can provide meaningful safeguards without unnecessarily restricting defenders.

Preparing for autonomous systems

Organizations cannot improvise security once an autonomous system begins acting unexpectedly. The necessary controls must already exist.

This includes deny-by-default network access, workload-specific identities, short-lived credentials, segmentation between evaluation and production environments, telemetry that can correlate activity across systems, and rehearsed containment procedures. Organizations should assume that a capable autonomous system will discover every reachable path that advances its objective and ensure that no such path grants unintended authority.

These incidents should be viewed as proof that security architecture must develop alongside rapidly advancing autonomy. Organizations that adapt now will be better positioned to harness the benefits of AI tools while keeping risk under control. Those that rely on instructions alone may discover that technology follows the paths we leave open, rather than the rules we intended it to follow.

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Business leaders, governments, and tech giants are locked in a high-stakes race for AI supremacy, fueled by debates over the destructive power of superintelligence. In this sort of arena, it’s essential that enterprises stay grounded and resist the urge to start a race of their own, deploying ever-larger models simply for the sake of it.

In the context of enterprise AI, there is no prize for launching the most powerful model first, building the highest number of agents, or forcing AI into every workflow. True success is defined by solving genuine business problems and delivering outcomes that justify the underlying cost and complexity.

While the prospect of a single, omnipotent large language model (LLM) is inspiring, today's business realities demand a more measured approach: every problem has a right tool, and often, that tool is a simple one.

AI doesn't have to mean LLMs

Before generative AI dominated the conversation, businesses were already using machine learning for recommendations, forecasting, fraud detection and estimating things such as the lifetime value of a customer.

We have had years to understand these technologies, including how to deploy and monitor them and make predictions at scale quickly and cheaply. An LLM may be capable of tackling some of the same problems, but that doesn't always make it the better option. More sophisticated models can cost considerably more to run and introduce variability that is unnecessary for a straightforward prediction.

Sometimes AI isn't necessary at all. If a process follows known rules and needs to produce the same result every time (moving data between systems on a schedule or triggering an action when a predefined condition is met, for example), conventional automation may do the job perfectly well.

Long before agents arrived, businesses handled these tasks with pipelines, schedulers, and rules. If that approach is reliable, fast, and inexpensive, adding an AI agent doesn't make the process more intelligent; it can simply make it more complex.

Generative AI earns its place when creativity or interpretation are required: understanding language or images, for example, or handling a process where the next step changes according to context. Agents can go further by using that understanding to interact with systems and take actions, opening up processes that were previously extremely difficult to automate.

The future is likely to combine conventional automation, traditional ML, generative AI and agents, rather than one replacing everything that came before it.

The AI honeymoon is ending

These distinctions matter more as enterprise AI moves beyond experimentation. A proof of concept can demonstrate what a model or agent is capable of, but production introduces running costs, security, monitoring and the work required to keep increasingly autonomous systems under control.

An engineer once gave me an analogy that captures this well. Imagine growing a tree that you need to maintain in a particular shape. When a branch grows in the wrong direction, you cut it.

Then another appears elsewhere. The difficult part is finding all the branches. Developing an agent can be similar. Obvious behaviors can be controlled relatively quickly, but real-world use exposes edge cases that weren't visible during development.

The more freedom a system has to interpret information and make decisions, the more important evaluation, monitoring and guardrails become. Control needs to extend beyond model behavior to the data a system can access, the actions it is authorized to take, the information it retains and the point at which a human can intervene.

There is also cost to consider. The most capable models can be expensive at scale, so additional capability needs to create enough additional value to justify it. As experimentation gives way to scrutiny of ROI, businesses will become less interested in whether something can use AI and more interested in whether AI actually leads to an improved commercial or operational outcome enough to justify the cost.

Let the data decide

Avoiding over-engineering starts with the business need and the data, rather than choosing the technology first.

Data engineers and analysts are crucial because they understand where information comes from, how reliable it is and what it can actually tell you. AI and ML engineers can put mathematical models on top, but sophistication cannot compensate for data that doesn't support the task.

The data and desired outcome will often point towards the appropriate approach. Fast, reliable predictions from structured numerical data may be better suited to traditional ML.

Language, images and other unstructured information can make generative AI valuable, while processes that require human level understanding to trigger actions across different systems may justify agents.

And sometimes conventional software will remain the best answer. Starting with “we need an agent” and then looking for a problem to give it is the wrong way around.

The right people matter as much as the technology

Increasingly capable AI has also created an assumption that businesses will inevitably need fewer engineers. AI is certainly changing productivity: experienced engineers can complete work faster with these tools, while junior engineers who learn to use them effectively can become productive more quickly.

But businesses still need people who understand what is happening underneath. I like to compare it with smartphones. Most of us use a phone effectively without being able to explain the hardware, operating system and networks behind it. That doesn't mean the underlying engineering skills have disappeared. Somebody still needs to understand those systems.

AI is likely to develop in a similar way. Many people will become highly capable users, while businesses will still need engineers with the deeper technical knowledge to build, control and fix the systems underneath.

That means continuing to invest in junior talent too. Senior engineers eventually move companies, change roles or retire. Organizations still need mentorship and knowledge transfer so today's juniors become tomorrow's senior engineers.

The opportunity isn't people or AI, but the two together. Capable engineers equipped with these tools can achieve considerably more than either could independently.

The next AI advantage will be restraint

The wider battle over superintelligence and increasingly powerful models will continue, but it shouldn't dictate how enterprises make technology decisions. To make this technical restraint viable in practice, engineering leaders must actively manage executive and board-level fear of missing out, turning top-down pressure for a visible ‘AI strategy’ into discipline, value-driven execution.

Some business problems will justify sophisticated AI tools. Agents will make previously impractical forms of automation possible, while generative AI can transform how organizations work with language, images and unstructured information. Others will be solved perfectly well by traditional ML, conventional automation or straightforward software.

Knowing the difference will become increasingly valuable. Competitive advantage will come from knowing where AI materially improves an outcome, where a simpler technology is more effective and how to move the right systems into productions securely and economically.

That requires good data, strong engineering talent and enough technical judgement to resist complexity simply because it is available. In the next phase of enterprise AI, restraint will not signal a lack of ambition, instead it will be a sign of technical maturity.

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The CMA has published roadmaps for Big Tech before, just not for Microsoft. With £400 billion in annual public sector procurement and millions flowing to Microsoft’s cloud, the CMA can’t wait until February 2027 to act.

The topic of public procurement is becoming a major issue for UK economic policy. Sarah Cardell, the CMA's Chief Executive, delivered a speech at a Parliamentary event at the House of Commons on 8 September, introducing two reports by the CMA published that day: Public Procurement in the National Interest and Rigged Bids, Real Costs.

The headline conclusion is, perhaps unsurprisingly, that UK public procurement could be leveraged far more effectively to shape markets and improve outcomes for citizens, businesses and taxpayers alike. But a more nuanced point, and arguably the most important one, is that the biggest risks arise when short-term cost savings are prioritized at the expense of long-term value and competition.

Cloud computing is one of the starkest illustrations of this trade-off in practice. Buying Microsoft is, in the immediate term, the path of least resistance: it is already embedded in most public-sector IT estates, integration is straightforward, and the near-term cost of switching or diversifying looks far higher than the cost of simply renewing. That short-term logic is precisely why Microsoft has wound itself so tightly around the machinery of UK public-sector cloud infrastructure.

Each individual procurement decision may look like sensible, low-risk value for money at the time it is taken but cumulatively, those decisions have entrenched a single supplier so deeply into public-sector data, identity and security architecture that it now poses a very real threat to competitive procurement in public-sector AI. The apparent saving today is a major liability for tomorrow, once every AI rollout has to be built on top of infrastructure only one vendor fully controls.

A market out of balance

The CMA's cloud market investigation reached a clear verdict in July 2025: competition wasn't working well. Microsoft and AWS each hold 30–40% market share with Microsoft's share still growing, and barriers to entry including data-center costs, economies of scale, incumbent product breadth. have been deemed likely to keep it that way.

Apparently, fewer than 1 per cent of customers switch cloud providers annually, held back by egress fees and technical friction. Most tellingly, the CMA found Microsoft withholds certain licensed products from AWS and Google that directly weaken those rivals; a problem with clear implications for public-sector buyers seeking competitive procurement.

A new AI market, losing balance

However, as Microsoft rapidly rolls out new AI tools across UK enterprises and the public sector, the fundamental issue of cloud infrastructure lock-in is being overlooked. The true barrier to a competitive AI market isn't just the stickiness of new daily workflows, but the interconnected stack comprising software, data, identity, security and the foundational grip Microsoft maintains through its legacy enterprise software, specifically Windows Server and SQL Server.

Once organizations are penalized for running these essential core workloads on rival clouds, their entire data gravity shifts permanently into Microsoft's ecosystem. Building subsequent AI processes and agents around these locked-in data structures makes opting for competing, innovative AI solutions financially and technically unviable.

The CMA has already diagnosed how Microsoft uses these core licensed products to weaken rivals; they must urgently address this foundational lock-in before it permanently closes off public procurement for enterprise AI as well.

Progress, but is it too cautious?

The CMA hasn't been idle. On 31 March 2026, the CMA accepted voluntary commitments from Microsoft and AWS rather than the binding designations recommended by its own investigators. These commitments included extending Microsoft's free egress-switch window from 60 to 180 days and a new cloud interoperability request mechanism, with a six-month review promised.

It simultaneously opened a strategic market status (SMS) investigation into Microsoft's entire business-software ecosystem including Windows, Office, Teams, and Copilot, encompassing over 15 million UK users with a decision due around February 2027.

That's a fair, if cautious, step, and critics such as the Open Cloud Coalition have welcomed it while pressing for swift action if commitments aren't met. Microsoft can reasonably argue these were offered in good faith to head off heavier intervention, but we need to start seeing these promises come into fruition quickly before further entrenchment occurs. Compare an AI rollout announced in June and largely complete by October against a regulatory clock that’s usually measured in years.

A roadmap for competition and social value

The CMA is now dealing with two closely connected issues: how public procurement can deliver greater value for taxpayers and society, and how Microsoft's market position can limit competition and choice. An interim roadmap could help address both at once. It would give public bodies clearer guidance on what to consider when procuring cloud and AI services, while setting out how the CMA intends to tackle the underlying lock-in that can narrow those choices.

This would support Andy Burnham's wider focus on delivering social value in public procurement. A more competitive and open cloud market would not just benefit businesses. It could give public bodies greater flexibility, reduce the risk that short-term savings lead to expensive, irreversible dependence on one supplier, and deliver better taxpayer value at the same time.

Certainty helps everyone

The roadmap should cover non-discriminatory licensing on rival clouds; interoperability extending to identity, permissions and agent portability; public-sector contracts with built-in exit plans; and independently verifiable evidence, not just documentation, that voluntary commitments are producing real switching and multi-cloud adoption. It should also provide broader transparency about how the CMA will assess whether these measures are working in practice.

Microsoft, its customers and its competitors all have an interest in predictable rules. Microsoft would know which integration practices remain legitimate; public bodies could invest in procuring AI at scale knowing it will not close off tomorrow’s choices, and rival cloud and AI suppliers would know if building interoperable products for the UK market is commercially worthwhile.

The CMA has diagnosed the problem, taken a first cautious step, and opened the right investigation. It should now set out the destination, the milestones and the tests for success before another year of AI adoption makes Britain's cloud and workplace-software markets harder to reopen. Without a clear roadmap or tests for success, we risk anti-competitive behavior leaking from the cloud market into deeply embedded AI work environments, at a far higher cost for the UK public sector to rewind.

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For small business owners, an unexpected payment might look like good news. A new customer has placed an order, an invoice has been settled, or an unfamiliar transaction has landed in the account. But when money arrives without an obvious explanation, the smartest response isn't to celebrate, It's to investigate.

Payment scams exploit the everyday pressures of running a business. Entrepreneurs are busy, cash flow matters, and customer relationships depend on responding quickly and professionally. Fraudsters understand this, using unexpected payments, urgent messages and convincing instructions to persuade business owners to make decisions before they've had time to check the facts.

And unfortunately, the risks are becoming harder to ignore as criminals use increasingly sophisticated digital tools to make these communications more convincing and their scams easier to scale. For small businesses without dedicated fraud teams, knowing what to look for, and having the right safeguards in place, can make the difference between a suspicious transaction and an extremely costly mistake.

Why do "random" payments happen?

An unexplained payment isn't automatically fraudulent, but it should always deserve scrutiny. Scammers can use unexpected transfers as the starting point for several different schemes, exploiting the assumption that money received is money earned.

One common tactic is the refund scam, where a fraudster sends money to a business, apparently by mistake, and then contacts the owner asking for it to be returned. They may apologise profusely, invent an urgent personal emergency or insist that a quick refund is needed to resolve an accounting error. The catch is that they ask for the money to be sent through a different channel, such as a bank transfer or gift cards.

The original payment may have come from a stolen account or compromised payment card, so if the legitimate account holder later disputes the transaction, the initial payment can be reversed, leaving the business out of pocket for the money it sent back.

Another possibility is testing stolen payment cards or hacked accounts. Criminals may use compromised payment details to make small purchases or transfers, checking whether the credentials still work. An apparently legitimate transaction could therefore be part of a wider fraud operation, and if the real cardholder disputes the payment, the business may face a chargeback, fees or the loss of goods already supplied.

Then there are phishing setups, in which the payment itself is little more than bait. Once the recipient contacts the sender, the fraudster may provide a link to a fake payment portal, request login credentials or ask the business owner to confirm sensitive information. What begins as a question about an unexplained transaction can become an attempt to take over an account.

The common overall thread is manipulation - the scammer wants the business owner to act quickly, bypass normal procedures or share information they would ordinarily keep private that they can now exploit or sell on to the highest bidder.

The warning signs every business owner should know

Although payment scams can take different forms, they often share a recognisable set of red flags. Spotting them early gives businesses the opportunity to pause before money or sensitive information changes hands.

Look out for these warning signs:

• An urgent request for a refund, where the sender insists the money must be returned immediately, perhaps citing a deadline or personal emergency.
• Pressure to use a different payment method, where the scammer asks you to return money by bank transfer, gift card or cryptocurrency instead of using the original payment platform.
• Instructions delivered outside official channels, such as unexpected emails, messages and links which direct you to alternative websites or ask you to disclose account details.
• Payments that don't make business sense. A round-number payment arrives that doesn't match an invoice, order or product price, or the sender's details don't match the customer record.

The key principle is simple, but usually effective: never assume that money received is money safely yours to return or spend. Check the transaction independently through your payment provider or bank, and never follow refund instructions supplied by an unverified sender. If a payment looks suspicious, contact the provider through its official website or app and ask how to handle it - never initiate a separate repayment yourself.

Building a stronger defence against payment fraud

Avoiding these scams doesn't require a dedicated fraud department, but it does require vigilance alongside consistent processes and a willingness to verify unusual requests rather than act on instinct.

One method is to ensure you always keep business and personal finances separate. Dedicated business accounts make transactions easier to reconcile and suspicious activity easier to identify, and mixing personal and company payments can obscure unusual transfers and potentially expose personal finances to business-related fraud.

Another useful protection is understanding what your normal transaction patterns look like. Keeping track of typical order values, customer locations, payment methods and refund volumes should be common practice - but although a transaction that falls outside these patterns isn't necessarily fraudulent, it may deserve a closer look. Reconcile payments against invoices and orders regularly, and investigate discrepancies promptly.

Securing the accounts that control your money will also go a long way. Make sure you always use strong, unique passwords for banking, payment platforms and business email accounts. Enable two-factor authentication (2FA) wherever available, and turn on alerts for large transactions, password changes and new logins, and where possible, limit account access to employees who genuinely need it.

Giving your team clear rules is another key step, as anyone who handles customer payments should know that unexpected refunds require verification. Establish a policy that prohibits returning unexplained payments through alternative channels, and encourage staff to check suspicious messages with a colleague before responding. Regular training can help employees recognise fake invoices, phishing links and attempts to impersonate customers or suppliers.

Finally, recognise that payment fraud often begins outside the payment platform itself. A fake invoice might arrive by email, a fraudulent website might imitate a trusted service, or a phishing message might steal the credentials needed to access a legitimate account. That's why good financial procedures need to be backed by effective cybersecurity.

Why Bitdefender Ultimate Small Business Security is a valuable partner

For small businesses looking to bring these safeguards together, Bitdefender Ultimate Small Business Security offers a practical way to strengthen protection without having to build an in-house security operation.

The service combines antivirus and device protection with email security, password management, VPN access, digital identity protection for all employees and business asset exposure monitoring. Its scam protection features are particularly relevant to payment fraud, helping users identify and block suspicious messages, links and websites.

Its Scam Protection Pro tool allows employees to check a suspicious message or link before responding, clicking or paying, offering an additional layer of scrutiny when an unexpected payment request or refund instruction doesn't feel right. Web and email protection can also help guard against phishing attempts designed to steal credentials or direct employees towards fraudulent sites.

Centralised management makes it easier for business owners to oversee protection across employees and devices, while support for Windows, macOS, Android and iOS reflects the reality these days that key business activity rarely takes place on a single machine or ecosystem.

Financial controls, and suspicious payments should always be verified with the relevant bank or payment provider, but combining those controls with cybersecurity tools designed to detect digital scams can make it much harder for fraudsters to exploit a moment of confusion.

For start-up owners entrepreneurs, the lesson is clear: an unexplained payment is a reason to stop and check, not an invitation to act quickly. With consistent procedures, informed employees and a comprehensive security solution such as Bitdefender Ultimate Small Business Security, small businesses can reduce their exposure to payment scams while keeping their attention where it belongs - on serving customers and growing the business.

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• Google leads efforts to dismantle 1.3 million device residential proxy botnet built through malware preinstalled on Android hardware
• Researchers say manufacturer Videostrong intentionally shipped malware and sold botnet access to cybercriminals
• Google seized infrastructure, blocked the company from Android certification, and strengthened device security checks

Google has revealed its role in taking down a massive residential proxy network and the malware that built it - with the unusual twist that the developer was a seemingly legitimate business with multiple products and countless trusting customers.

In a blog post, the company said that together with its partners, it dismantled a backdoor called XCPE. This malware was apparently used for ad fraud, served as a dropper for additional malware, and used for assimilating devices into an illegal residential proxy network.

In total, more than 1.3 million set-top boxes alone were part of the network, the company said.

No supply chain attack

Malicious residential proxy networks are nothing new - they emerge and get dismantled on a regular basis. But this one was unique, as it came preloaded on Android devices built and sold by a certified Android Original Equipment Manufacturer (OEM) and Original Design Manufacturer (ODM). In other words, people were thinking they were buying a legitimate product from a legitimate, Google-vetted company, but were instead getting malware-laden hardware that abused their trust and sold their bandwidth to cybercriminals.

In this case, not only did the company know what it was doing, but it was actively developing the malware and even worked on hiding it from Google and everyone else.

“A device manufacturer intentionally infecting their users with malware is an unacceptable breach of trust, and represents a serious risk to those users,” Google said, not hiding its disappointment with the manufacturer.

Videostrong ended up selling two things: On the surface, it was selling Android-powered smartphones and similar devices that contained malware that built a botnet. Under the table, it was selling access to that botnet to cybercriminals.

Google was joined by a number of other companies and agencies in this operation, including Japan’s National Police Agency, Lumen, HUMAN Security, Plume, Synthient, and TrendAI.

Threat actors are always in need of residential proxy botnets. They can use them to launch powerful Distributed Denial of Service (DDoS) attacks, hide their addresses as they operate, conduct large-scale credential stuffing campaigns, bypass geographical restrictions, scrape websites at scale, and more. We don’t know how much Videostrong made from selling access to the botnet, or who used it.

Taking down the C2 domains

To disrupt both the malware and the network, Google took down the domains and servers the company used to control the botnet and install malware modules, took down C2 domains that were used by over a dozen malware families loaded as modules via the XCPE backdoor, and banned Videostrong and all future subsidiaries and affiliates from building and selling Android devices in the future, or supplying software to other certified vendors.

Google also said it had improved its firmware integrity checks for certified Android TV devices to prevent similar incidents involving other manufacturers.

“Google requires OEMs go through a comprehensive suite of compatibility and security tests to ensure a consistent and safe user experience across the Android ecosystem,” it stressed. “However, Videostrong employed obfuscation techniques to exploit a gap in our tests and load malicious code after the device's first boot. As a result, Google has implemented changes to its existing review process to better safeguard against similar activity in the future.”

This is not the first time an electronic device came with malware off the shelf. Last month, security researchers Bitdefender found Android phones from multiple manufacturers, including Doogee and Cubot (both Chinese firms) being sold with preloaded malware. However, in those cases, the supply chain was compromised and the malware was included without the OEM’s knowledge, or consent.

OpenAI chief scientist Jakub Pachocki seemed to issue a warning on September 6.

In “An Alien Mind,” he argued that current alignment and monitoring techniques may soon prove inadequate for the pace at which machine intelligence is advancing. He also described a future in which AI takes a growing role in improving AI, compressing the time available for human supervision and institutional response.

Much of AI safety research concentrates on behavior. For example, what an AI system is permitted to do, how its actions are monitored and where humans retain approval. Yet every action begins one step earlier, with a judgment about what information deserves belief.

That earlier step is incredibly important.

AI acts on information whose pedigree is often obscure

Before an AI sends a payment, rewrites software or recommends a decision, it first assembles a picture of the world. That picture can draw on search results, corporate records, research papers and material produced by other AI systems.

The web was built for circulation. Information travels easily, while its ancestry often fades as it passes from one publication to another. A claim can begin in a research paper, appear in a news story, be condensed into a blog post and surface months later in a forum thread. By the time an AI encounters the statement, the route back to the original evidence may be difficult to reconstruct.

Authority also gets compressed. A peer-reviewed study, a corporate announcement and an anonymous post can all arrive as text. An AI system can rank and combine them into a polished answer, while the reader sees little of the difference between documented evidence, advocacy and repetition. Disagreement adds a further complication.

Credible sources regularly reach different conclusions, especially in finance, medicine, politics and emerging technology. AI is usually rewarded for producing a useful answer, which creates pressure to reconcile conflict into a single sentence. Uncertainty becomes easy to erase precisely when it deserves to remain visible.

The consequences grew substantially once AI acquired the ability to act. An agent can place a trade, approve a purchase, alter software or send a message on someone’s behalf. An uncertain claim can therefore become the premise for an irreversible action.

Human oversight depends on better evidence

The effectiveness of human oversight depends on the reviewer having superior information or a reliable means of checking what the AI used. In many settings, the human reviewer has access to the same web that the AI searched, only at a fraction of the speed. The AI can scan thousands of pages while the person responsible for approval works through a summary and a handful of links. As capability rises, review risks becoming a procedural signature rather than an independent examination.

Pachocki writes that OpenAI’s ability to rely on chain-of-thought monitoring is diminishing as reasoning becomes more complex and as AI becomes more capable of manipulating its own reasoning process. He calls for safety thresholds that can be enforced by third-party auditors, government agencies or international bodies.

Those safeguards govern capability and behavior. Reliable oversight also requires an evidentiary record showing what information produced the conclusion, where each claim originated, how trustworthy the source is and where credible disagreement remains. Without that record, the reviewer is judging the answer more than the evidence behind it.

AI inherited the web’s weakest habit

AI absorbed an information culture built around speed, reach and replication. Search engines, social feeds, aggregators and reposts made access extraordinarily efficient. They also encouraged a world in which the same statement could appear hundreds of times while tracing back to a single uncertain source.

Generative AI intensifies that effect because AI systems now produce material that later AI systems can ingest. A weak claim can be generated, scraped, summarized and republished until repetition gives it the appearance of consensus.

This creates a provenance problem with compounding consequences. Every additional generation can separate a statement further from the evidence that gave rise to it. Once an autonomous agent treats that statement as reliable, the information chain becomes part of the decision chain. While for consumers, this can mean a poor recommendation, for a financial institution, insurer or government agency, the same weakness can affect money, access, security or public services.

Autonomous AI needs a system of provenance

Take finance. Serious financial activity depends on records that establish who acted, under whose authority, on which terms and with what audit trail. Institutions spend heavily on those records because trust requires more than a plausible outcome. Autonomous AI needs a comparable discipline for information.

Claims should carry provenance that survives copying and transformation. A person or an agent should be able to trace a statement back to its origin, inspect the evidence behind it and see how the statement changed as it travelled. Credible disagreement should remain intact.

Where reputable sources diverge, the system should preserve the competing views and the degree of confidence behind each one. For an autonomous agent, uncertainty can serve as a trigger for further research, human approval or a narrower course of action.

Accountability should travel with information as well. A claim from an identifiable institution with supporting evidence deserves different treatment from an unattributed assertion repeated across dozens of pages. The aim is to give AI a richer sense of evidentiary quality before it commits resources or acts on someone’s behalf.

Governance belongs at a shared level. Provenance standards, verification methods and accountability rules need independent scrutiny and participation from many institutions, rather than dependence on the preferences of a single provider.

Final thoughts

Pachocki argues that current alignment and monitoring techniques may soon constrain the pace at which labs can responsibly scale AI tools, and he expects voluntary slowdowns until shared safety thresholds emerge. He also warns that continued progress could carry AI into recursive self-improvement, with AI driving a larger share of its own development.

That leaves every company building autonomous systems with a fundamental task. Establish what the agent knows before it acts and make that knowledge auditable.

Behavioral safeguards remain essential. So do permissions, audits and human approval. Their value rises sharply when the information beneath every decision carries provenance, context and accountability. The next phase of AI safety, therefore, begins with the evidence an agent sees, the authority it assigns to that evidence and the uncertainty it preserves before taking action.

Autonomy built on verifiable information gives humans a stronger basis for control. Opaque information leaves supervisors judging decisions whose foundations remain difficult to inspect.

Pachocki is right to warn that the window for preparation may be narrow. The industry should use that window to build systems that can show where their knowledge came from before those systems are trusted to act on the world.

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• A zero-click Android flaw let attackers crash phones with a specially crafted SMS message
• Repeated messages could trigger reboot loops, disrupt calls, and even affect emergency connectivity
• Google patched supported Android versions, but many end-of-life devices remain permanently vulnerable

Picture this: You’re talking on your Android phone when all of a sudden you just lose mobile connectivity and your phone call drops. No explanation, no notification. Soon after, it happens again. And again. At one point, the device goes into recovery mode, rebooting every minute and a half until you finally perform a factory reset.

This is not a theoretical scenario - it is a real possibility, thanks to a critical-severity vulnerability in Android tracked as CVE-2026-58882.

Google has patched the issue for newer versions of Android, but those who are past their end-of-life date will remain at risk.

Sending an evil message

The bug was discovered by security researcher Mark Blah and described as a zero-click denial of service (DoS) vulnerability. In more technical terms, it is an “improper validation of a header-length value” bug in Android’s WapPushOverSms.decodeWapPdu function. It was nicknamed “SMS of Death” because it can be exploited with nothing more than an SMS message, which the victims doesn’t even have to open (hence the ‘zero-click’ part).

As Blah discovered, a threat actor could send a malformed eight-byte WAP Push message to an Android device’s port 2948: 01 06 88 80 80 80 00 BE.

Since this is a hex message, when it gets translated into a signed 32-bit integer, it becomes -2147483648. Android can then try to allocate memory using this negative value, triggering a NegativeArraySizeException error that crashes the com.android.phone process. Since this process manages cellular functions, the call is dropped and mobile connectivity is disrupted. Even emergency services are susceptible, it was said.

It gets worse. Android processes messages before they reach the inbox, meaning the victim will not be notified of the SMS and won’t even know it’s the text message that’s crashing the system. Also, some carriers will attempt to redeliver messages that never reached the inbox, extending the agony. Android also does its part by constantly trying to process messages already stored on the phone.

Finally, Blah found that if a malicious actor sends 20 or more messages, they can force the device into a reboot loop. On an Android 12 device, they can force looped recovery mode, leaving factory reset as the only viable option.

Patching the SMS of Death

Initially, all Android versions were affected, it was said. Google patched it for versions 14 through 17, but older devices will not be getting a patch and thus will remain at risk indefinitely. The fix is called “Fix remote DoS in decodeWapPdu”, and apparently it prevents the crashes by catching exceptions triggered when Android processes malformed text messages. It also deletes these messages from the SMS database so that they don’t trigger repeat crashes.

Blah has released a proof-of-concept (PoC), showing the wider community exactly how to exploit the flaw, and BleepingComputer reported that a GitHub contributor found a way to remove the malicious message, but it requires more technical knowledge, which is something the majority of mobile phone users probably won’t have. Still, those interested in tinkering should know that it can be removed via a computer, using Android Debug Bridge (ADB).

Google apparently patched the bug in its October 2026 Android security updates, but that doesn’t automatically mean all Android users are safe. Unlike Apple’s ecosystem, Android is diversified, and different manufacturers have different update cadences.

Therefore, while we can assume Pixel users have likely already received this update, Samsung users, as well as those using Xiaomi, Motorola, or OnePlus devices, will probably have to wait a little longer until their manufacturers apply the fix to their devices and push the update.

Still, since the PoC has been released, it’s safe to assume most Android manufacturers have already pushed their updates. Users are advised to install them as soon as possible.

• New malware uses AI to analyze stolen data, rank victims, and identify the most profitable targets
• VOLTA-AUTODOXER steals credentials, crypto wallets, tokens, and personal information before generating AI dossiers
• Researchers warn the malware could commoditize AI-powered victim profiling for ransomware and cybercrime operators

Stealing sensitive data from a lot of people is one thing, but determining whether or not that data is worth anything to anyone - that’s an entirely different ball game.

Now, it seems cybercriminals found a way to easily profile their victims’ information and they’re using - you guessed it - Artificial Intelligence (AI).

Cybersecurity researchers Cisco Talos uncovered a new malware strain through Cognitive Artificial Intelligence Research Network (CAIRN) - an open source cybersecurity research kit built to identify and track AI-powered malware.

The strain is called VOLTA-AUTODOXER and its operation is split into four key steps: 1) steal as much information as possible; 2) share it with an AI for assessment; 3) exfiltrate the data to attacker-controlled infrastructure; and 4) remove yourself from the victim’s system.

Analyzing the data

Cisco Talos did not say where exactly the malware was found, how many devices it infected, or how it is being disseminated. It said that, on surface level, it looks just like any other infostealer. It grabs browser passwords, cookies, payment cards, cryptocurrency wallet seed phrases, Discord, Telegram, Steam, and Roblox sessions, Microsoft OAuth tokens, SSH and cloud-provider keys, Wi-Fi profiles, and screenshots.

But this is where it splits from the industry standard, by building a structured evidence bundle about the person behind the system.

It pulls the device’s geographical location, home address, employer and employer domain, inferred birth year, the last 4 digits of their credit cards, Slack workspaces, document authorship metadata, iPhone backup indicators, and even a live Roblox account valuation. Then it moves to Gmail, looking for mails from UberEats and DoorDash in order to confirm the home address.

VOLTA then bundles and serializes the bundle, before POSTing it to OpenRouter, an AI infrastructure platform where users can access hundreds of LLMs through a single API.

There, the AI gives a model a prompt to behave as a “forensic intelligence analyst drafting a target assessment”. In other words, the AI is tasked with assessing whether or not the data pulled is worth any trouble, and if so - how much. Victims are ranked by value, giving the attackers more time to do what seems to be the end goal here - ransomware.

The default model is Claude, but if it refuses to comply for any reason, the malware is instructed to fall back to Moonshot AI’s Kimi K2 and Google’s Gemini 2.5 Flash. If neither of the models end up working, the malware will just send raw data back to the attacker. This is usually done via a Telegram channel.

Running away

After the work has been completed, VOLTA is instructed to delete itself from the system, likely to cover its tracks. A separate file, named volta_debug.txt apparently remains on the system, showing what was collected and whether or not the AI-powered assessment was a success.

The researchers believe VOLTA is designed to be sold on the black market. Elements such as the Telegram channel, or API keys, are not hardcoded, and the malware comes as a template with six swappable slots that are filed in at build time.

“This suggests it’s a builder model: The author sells access, the customer provides their values, and the builder compiles a custom binary for each order,” Cisco Talos explains. “If VOLTA is indeed a commoditized malware, then the AI dossier capability isn't something the author invented for their own operations. It is a feature which enables a customer of any level of sophistication to point it at victims and start receiving ranked target assessments in their Telegram inbox.”

If you suspect an infection, revoke or rotate all API keys and tokens, and change all of your passwords. Also, double-check your crypto wallets and move all funds to a new wallet with a fresh seed.

• The FBI formally attributes $6.7 billion Binance cryptocurrency theft to North Korea’s TraderTraitor group
• Investigators say the attackers are laundering stolen assets across multiple blockchains and DeFi services
• The breach highlights ongoing threats from Lazarus-linked campaigns targeting crypto, DevOps, and software professionals

The FBI has publicly accused a North Korean state-sponsored threat actor of being behind the recent attack on cryptocurrency exchange Binance.

One of the largest cryptocurrency exchange platforms on the planet was forced to freeze deposits and withdrawals, before confirming a threat actor stole around $6.7 billion worth of different cryptocurrencies.

Analysts were quick to investigate, with some claiming a known North Korean state-sponsored group called TraderTraitor was behind the heist. TraderTraitor is apparently part of Lazarus Group - a huge cybercriminal operation on North Korea’s payroll, allegedly tasked with stealing money to fund the country’s state apparatus and weapons program.

TraderTraitor

Now, the FBI has issued a new statement, official naming TraderTraitor as the attackers.

“The Federal Bureau of Investigation (FBI) is releasing information advising the Democratic People's Republic of Korea (North Korea) was responsible for the theft of more than $6 billion USD in virtual assets from cryptocurrency exchange Binance, on or about October 29, 2026. The FBI refers to this specific North Korean malicious cyber activity as ‘TraderTraitor’.,” the announcement reads.

“The FBI, together with partners at the USSS and IRS-CI, has observed TraderTraitor actors converting stolen assets primarily into Ethereum (ETH). It is expected these assets will then be further laundered by converting them using DeFi and instant swap services as they prepare them for further laundering stages.”

Binance said one of its DevOps engineers had their laptop compromised which “gave an attacker access to a service container, where they submitted unauthorized withdrawal transactions.” Some speculated the engineer fell to a classic fake job scam, while others argued it was a romance scam.

In the meantime, Binance resumed operations and has covered most of its losses.

Initial reports found that the attackers siphoned out money from 10 different chains, including Bitcoin, Ethereum, Solana, XRP, Arbitrum, and others. For Ethereum, they even set up a smart contract that automatically withdrew any new money that was flowing into the account. The silver lining in the attack is that these were not user funds, but rather those of the exchange itself.

Cryptocurrency exchanges are obliged to have two separate wallets - one for their own funds, and one for those of their customers. Binance has a security fund called SAFU (Secure Asset Fund for Users), designed to protect user assets during extreme platform-level incidents, such as this one.

Operations DreamJob and Contagious Interview

Obviously, North Korea has not commented on these allegations, but the cybersecurity community has logn warned about Lazarus Group targeting organizations in the West, sometimes tasked with stealing money, and sometimes with cyber-espionage and eavesdropping.

The group is split into numerous sub-groups, one of them being TraderTraitor, and among other things, the group runs two major scam campaigns: Operation DreamJob, and Contagious Interview. Both are aimed at software developers, blockchain programmers, and DevOps professionals, and the wider software development industry.

As part of these scams, the attackers would create fake companies, fake employees, and fake job offers. They would reach out to their targets on LinkedIn and offer lucrative job positions, only to infect their computers with malware during the interviews. In some cases, they would apply for open positions, impersonating Chinese, Vietnamese, or Thai nationals and would, when they get the job, use the position to steal sensitive data and money.

North Korea is under heavy sanctions from the US, and the country does not allow its companies to hire, or do business with, individuals and organizations in North Korea.

The FBI shared a long list of wallets it believes contain the stolen funds, and has asked THORNode operators, RPC node operators, instant exchanges, DeFi services, blockchain analytics firms, and other virtual asset service providers to block transactions with or derived from these addresses.

AI innovation took center stage at IFA Berlin this year, with unprecedented solutions operating alongside vastly enhanced products powered by AI. Exhibitors showcased everything from advanced humanized assistants designed for daily chores to AI-driven robotic pets offering emotional companionship.

It will take time for these products to be honed and readily available for consumers, but many innovations are already here. Hisense demonstrated its AI companion suite, which will transform user interaction while Miele’s flagship lineup highlighted the role AI, advanced sensors and connected systems can play in minimizing decision-making in daily tasks.

While these products may be received well by tech loving audiences, not all consumers are convinced. When these products make it to market, brands and retailers are likely to be faced with cynicism, safety concerns and complexity-driven confusion. To overcome these challenges, innovators must consider the customer experience.

Overcoming the information gap

When it comes to big-ticket tech purchases, consumers want to understand what they are paying for. High spec products are increasingly complex, and many consumers do not have the technical know-how to understand the differences between products and the potential benefits of new AI-enabled features.

In product descriptions and marketing campaigns, brands often prioritize the ‘cool factor’, focusing on innovative new features. While techy audiences take this in their stride, this often alienates consumers who are less confident in their knowledge. These audiences need straightforward explanations of what the product does, how AI or tech has been incorporated and how it can enhance their lives – but they are often hard to find.

Unfortunately, some companies take advantage of limited understanding, slapping misleading ‘AI’ labels on their products, which ultimately do not deliver. This disappointment and negative word-of-mouth these customer experiences generate, along with high-profile hacking horror stories and broader concerns about data security, have impacted trust – and a lack of transparency and information only adds to the nagging feeling that companies have something to hide.

Companies that are honest and open about how their products incorporate AI, the benefits these features offer and how data is used and stored, can start to rebuild trust and earn the respect of consumers. While exciting high-tech features remain an important marketing tool, a ‘back to basics’ approach that offers easy-to-find answers to common questions and addresses concerns in clear, accessible language can help consumers to understand if a product meets their needs and to feel more comfortable making a purchase.

Consider all channels in the retail ecosystem

Although discovery and research often take place online, more than four in five (85%) consumers prefer to buy electronics in-store. Bricks-and-mortar locations provide the opportunity to see or trial products in person, receive answers to unresolved questions and seek advice from sales assistants – and demand will only increase as products become more complex.

While consumers may have a good idea of their preferred product when they enter a store, their decision can change quickly if the experience falls short of their expectations. When shoppers go into a store and cannot find anyone to assist them, they may abandon their purchase or feel undervalued.

Despite the importance of the in-store experience, many tech brands are leaving a gap in the funnel. Once goods reach a physical store, the retailer is often trusted to convert intention into action. However, with physical retail coming under pressure due to rising operational costs, many are reducing headcount, which makes it harder for consumers to get the help and advice they came for.

Lean into education and advocacy

As AI makes products more complex, availability of information and access to in-depth knowledge is becoming a necessity rather than a nice-to-have. When consumers are uncertain, sales will falter and there is a far higher risk of buyers’ remorse.

Retailers must be equipped to convert the confusion of the average shopper into purchases, but training budgets are being cut alongside headcount, with in-person practical training being replaced by online learning. Digital updates can provide a solid foundation, but surface-level modules do not inspire teams’ confidence or build their ability to sell.

Many retailers are unable to fund and provide the training required, but brands have an opportunity to step in. Brands with more complicated products should consider placing brand ambassadors in store or providing additional training to stockists’ staff to ensure that someone is on hand to answer challenging questions, reassure shoppers and help them find the model that will strike the best balance between their needs and their budget.

Experience is make-or-break for tech brands

Brand equity and goodwill are vital for long-term commercial success. Negative experiences detract from trust in both the retailer and the brand, while positive interactions help to solidify it. Brands should monitor sales and reviews closely, identifying areas for improvement, and work closely with retail partners to address any potential issues at the point of sale.

The cost of providing accurate, helpful information online or in-store is negligible, while the cost of ambassadors and training far outweighs the cost of potential failure and subsequent delisting of a brand and its products. Failure to offer in-depth details, expertise and advice can have a sizeable impact on the bottom line: making customers feel comfortable is the only way to ensure AI-enhanced products deliver the expected returns.

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• Applebee’s fixes mobile app flaw that exposed customer data through hardcoded database credentials
• Researchers found access to personal information including names, contact details, birthdays, and account metadata
• No evidence of abuse was found, but the incident highlights the persistent risk of hardcoded secrets in software

Applebee’s has fixed a serious flaw in its mobile app that exposed sensitive customer information to pretty much anyone. Whether or not anyone malicious actually found this vulnerability and exploited it is not known at this time.

The chain has an Android mobile app that allows customers to browse the menu, customize meals, place orders for delivery or pickup, save their favorites, or receive personalized food recommendations.

A security researcher from What Security called Robert Wiggins, used AI to analyze the source code of the app and found a gaping hole - hardcoded credentials for a company database.

Notifying the business

The database contained plenty of sensitive information: customer names, their email addresses (both verified and unverified ones), dates of birth, phone numbers, ZIP codes, veteran status, the number of logins, dates of account creation, and the IP address of the most recent login session.

We don’t know exactly how many people had their sensitive data exposed that way, but the app has more than a million downloads on the Google Play Store.

Wiggins soon realized that there was no way to report the issue either to Applebee’s itself or to its parent company, Dine Brands, so he reached out to TechCrunch. The publication verified his claims by finding the exposed credentials itself, and even created a new entry which Wiggins was later able to extract, and also confirmed the credentials allowed users to modify database entries.

TechCrunch managed to get ahold of Dine Brands, who plugged the hole and thanked everyone involved: “We appreciate TechCrunch and the security researcher for bringing this matter to our attention,” the company said. “Our security team promptly launched an investigation and has not currently found any evidence of unauthorized access.”

“Our investigation is ongoing, and we will take appropriate steps based on our findings,” the company added.

Hardcoded credentials are a very common mistake that sometimes has dire consequences. During production, software developers would leave login credentials inside the code to work faster, allowing the app to skip the tedious process of identifying every single time. However, the credentials are supposed to be temporary and are usually removed from the code before launch.

Sometimes, though, the credentials don’t get spotted during final code review and end up in the live version. Crooks who scan the codes for vulnerabilities can then use the information for unauthorized access.

A common error

Unfortunately, it is a very common problem. In March 2025, security researchers from Cybernews analyzed more than 156,000 iOS apps and found more than 815,000 hardcoded secrets, thousands of which were “very sensitive and could lead directly to breaches or data leaks.” It’s worth mentioning that in this case, a “secret” means more than just passwords - it also includes API keys and encryption keys. The average app's code exposed 5.2 secrets, and 71% of apps leak at least one secret, Cybernews reported at the time.

A few months later, in July, HPE patched a similar bug in Aruba Instant On Access Points, and at roughly the same time, a similar flaw was found in Cisco’s Unified CM, enterprise-grade IP telephony call control platform providing voice, video, messaging, mobility, and presence services.

The leaks continued in 2026 - in February, news broke of Chinese state-sponsored threat actors abusing hardcoded credentials found in Dell’s RecoverPoint for Virtual Machines.

Applebee’s did not notify its customers about the incident, and it did not publicly disclose it on its website or social channels, at press time. Also, there are no indications that any threat actors exfiltrated this data or leaked it to the dark web.

• 'Thousands' of GCHQ storage devices are missing or unaccounted for
• Most are likely protected by encryption or PIN access
• Watchdog is 'concerned' and 'disappointed'

The UK's intelligence and cybersecurity agency, GCHQ, has been slammed by its very own watchdog after failing to account for thousands of missing storage devices.

The 2025 Investigatory Powers Commissioner's Office (IPCO) report ultimately concluded GCHQ has failed to establish the location or status of thousands of removable storage devices such as USB sticks, external hard drives and memory cards.

While the exact number of devices is unconfirmed, the IPCO report does confirm the estimate to be "in the thousands" – and that's a GCHQ estimate, not a third-party estimate.

GCHQ has seemingly lost thousands of USBs, external drives, memory cards

"There is a programme of further work to locate and account for as many of these devices as possible," the report adds, noting that "GCHQ will be able to report how many devices have been confirmed as lost."

Nevertheless, the IPCO expressed disappointment at how long investigations and remedial action had taken – the problems were actually first brought to the watchdog's attention years ago, so it's not a newly discovered issue. "We remain very concerned about this issue and are disappointed with the time it has taken GCHQ to investigate."

The issue was already raised in the IPCO's 2024 report, when the watchdog confirmed that it has asked GCHQ and MI5 to "review their internal allocation and audit of removable media" the year prior.

Back then, it was already acknowledged that many of the missing devices were either encrypted or required a PIN to access, however given the intense sensitivity of many of these artefacts, significant risk could still be on the cards.

The latest report acknowledged the technical and practical difficulties involve with the ongoing investigation, but calls for continued senior-level oversight." According to the report, GCHQ has commissioned a new tool to monitor the issue.

If you work in cybersecurity for long enough, you learn that we really love an analogy or a metaphor and come October you can barely move for talk of security nightmares, cyber monsters and threats lurking in the shadows.

I enjoy a good analogy as much as anyone, but there’s one Halloween comparison I don't hear very often. And, for data security at least, I think it might be the most useful one right now: the mask.

A mask doesn't make what's underneath disappear. It simply means you don't have to reveal its true identity to everyone who encounters it, and that’s what we’re trying to achieve with sensitive data.

Developers need data to test applications, analysts need it to find patterns, and AI tools and agents increasingly need access to enterprise information to do useful work. But needing to use this data isn't necessarily the same as needing to see every sensitive value it contains.

That's precisely where data masking comes in. By replacing sensitive information with realistic but unusable values, organizations can preserve the usefulness of data without unnecessarily exposing what's underneath.

With AI dramatically increasing where data can travel, who can find it and what can act upon it, that distinction is becoming much more important.

A disguise with a serious purpose

At its core, data masking replaces sensitive information with realistic but unusable values. The resulting dataset can retain characteristics necessary for development, testing, analytics and other business processes without unnecessarily exposing the sensitive information underneath. Think of it as a Halloween costume with a security purpose; the data can still show up to the party without revealing its true identity.

That distinction matters because protecting data shouldn't simply mean preventing people from using it. Modern businesses depend upon being able to analyze, test against and increasingly train or ground AI systems in enterprise information. Instead, the question should be: does this person, application or AI system actually need the original sensitive value to perform this task?

A developer testing an application may need representative customer data, for example, but not necessarily genuine customer identities. An analytics workload may need to preserve relationships and patterns without revealing every account number or personal identifier.

If the real value isn't required, exposing it creates risk without creating corresponding business value.

Masking removes that unnecessary exposure. Depending on the use case, organizations can also use techniques including tokenization and encryption. The important principle is to match protection to how the information needs to be used rather than defaulting to exposing raw sensitive data.

You can't mask what you can't find

There is an obvious complication: before deciding what should wear a mask, businesses need to know where their sensitive information is.

That remains surprisingly difficult.

The 2026 Thales Data Threat Report found only 34% of surveyed organizations had complete knowledge of where their data was stored. Only 39% said they could classify all of it. Meanwhile, the average organization reported using seven different tools for data protection and monitoring.

Cloud infrastructure compounds the problem. Information can be copied into data lakes, SaaS platforms, development environments and backups. Unstructured documents can contain sensitive information nobody realized was there. Applications created years after the data was collected can suddenly gain access to it.

AI makes that visibility problem considerably more consequential because finding information is precisely what these systems are becoming very good at.

This is where data security posture management (DSPM) becomes important. Rather than treating data security purely as a question of defending infrastructure, DSPM starts with the data itself: discovering and classifying sensitive information and understanding where it resides, who or what can access it, how it is being used and whether its protection is appropriate.

Once an organization identifies unnecessarily exposed sensitive data, it can decide whether access should be removed, restricted or protected using controls such as masking, tokenization or encryption.

AI data: trick or treat?

This becomes particularly important as businesses progress from experimenting with generative AI towards embedding AI into everyday operations.

AI applications need data. Agents may need considerably more of it because they are designed not simply to generate content but to retrieve information, make decisions and potentially take actions across different systems.

The security implications are already becoming visible. The same research found 61% of organizations reported their AI applications were being targeted by attackers, with sensitive data the leading target. Rapid change within the AI ecosystem was also the most commonly cited AI security risk.

The important question, however, isn't simply whether AI can be secured. Organizations also need to ask a more fundamental question:

What data are we allowing AI to see in the first place?

Giving an AI application access to a database shouldn't automatically mean giving it access to every raw sensitive value contained within it. The same least-privilege principle used for human identities can be applied to data: expose what is necessary for the task and protect what isn't.

That could mean masking personal information before it reaches development and testing environments. It could mean tokenizing particularly sensitive values used by an application. It could mean encrypting information and tightly controlling which identities can decrypt it. And in some circumstances, it should mean preventing an AI system from accessing a dataset altogether.

Don't make AI security harder than it needs to be

There is a tendency to treat AI security as an entirely new discipline requiring entirely new answers.

Some risks certainly are new. But one of the most useful things organizations can do is apply established data-security principles to this new environment.

Know where sensitive data resides. Understand its context. Minimize unnecessary access. Protect the underlying value according to how it needs to be used. And continually reassess those decisions as applications and AI systems change.

AI may dramatically increase how much value organizations can extract from their data. It doesn't follow that every system extracting that value needs to see everything underneath.

Halloween only comes around once a year. For sensitive data, however, having the option to wear a convincing mask year-round is becoming a non-negotiable.

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Walk into the network operations center of any major enterprise and you will see an array of monitoring capability. Massive dashboards track API response times to the millisecond and map digital customer journeys from an ad click on a smartphone all the way to a virtual shopping basket. On paper, enterprise IT has achieved total operational awareness.

Step onto the high street at twelve-thirty on a Friday afternoon, however, and that illusion dissolves. A payment terminal freezes at a busy cafe counter. The line extends out the door. The cashier tries rebooting the hardware while office workers check their watches.

Within minutes, shoppers begin walking out empty-handed, heading straight for the competitor across the street. Back at corporate headquarters, central monitoring dashboards remain entirely green. As far as IT knows, systems are operating normally.

This disconnect represents one of the most significant operational blind spots in physical commerce today. In the UK alone, checkout failures put an estimated £1.7 billion in annual sales at risk. When transactions stall during peak trading windows, that revenue is cannot be recovered.

The failure is rarely a collapse of the central cloud. Often, it is a telemetry blackout occurring at the critical physical touchpoint where money actually changes hands.

Why payment security creates blind spots

To understand how physical commerce ended up in the dark, one must look at the intersection of security compliance and legacy monitoring tools. For years, the gold standard of enterprise visibility has been Application Performance Monitoring. These platforms thrive in open cloud environments where software agents can trace code execution and inspect payloads across distributed microservices.

Physical payment infrastructure operates under radically different constraints. The Payment Card Industry Data Security Standard mandates strict network isolation to protect cardholder information. Point-of-sale software, payment applications and hardware PIN pads are deliberately locked inside hardened, encrypted subnets.

While non-negotiable for security, this architecture creates an operational dead zone. The moment a transaction leaves the standard corporate network and crosses the compliance boundary, visibility ceases. As the signal travels through local hardware, store routers, third-party gateways and acquiring banks, standard IT tools cannot follow.

Central teams only discover an outage when store managers log manual helpdesk tickets or when batch reconciliation reports reach the operations team hours later.

The cost of the seven-minute window

IT support teams measure incident resolution in hours, but in-store shoppers measure patience in seconds. Research tracking consumer behavior during in-store disruptions shows that shopper patience has a hard limit of approximately seven minutes. Once a payment delay crosses that mark, the customer abandons the purchase entirely.

Contrast this narrow seven-minute window with the standard lifecycle of an IT incident. The average payment disruption takes over sixty minutes to diagnose and resolve. By the time an on-duty manager contacts support, the ticket escalates to an engineering queue and analysts begin combing through disparate vendor portals to locate the failure, the high-traffic sales surge has passed.

In an era where cash use is in significant decline and frictionless contactless payments are expected, relying on post-incident reviews results in lost revenue.

Unlocking real-time payment telemetry

Solving this problem requires an architectural evolution from delayed, post-mortem log scraping to real-time telemetry streaming. The key insight lies in understanding that monitoring transaction health does not require exposing sensitive cardholder data. Enterprise architectures must cleanly decouple regulated financial data from operational metadata.

While the payload containing card numbers remains locked inside the compliance envelope, operational metadata, such as gateway response codes, handshake transit times, peripheral hardware status, and localized network latency, can be safely extracted and streamed in real time.

By transforming every swipe, tap and dip into an operational data feed, engineering teams gain an end-to-end view of the transaction pipeline. Site reliability engineers can finally monitor the health of physical checkout counters with the same fidelity they apply to their cloud applications.

Using AI for automated fixes

Streaming telemetry provides the necessary visibility, but human analysis alone cannot keep pace with the volume of modern commerce. During a peak holiday rush, an enterprise payment stack might process tens of thousands of transactions every minute across hundreds of distributed venues.

A subtle degradation, such as an obscure firmware glitch that causes contactless taps to time out on ten percent of devices, is impossible for a human operator to catch in real time.

This is where AI and edge observability can make a decisive difference. Rather than depending on static alerts that trigger after a catastrophic failure, machine learning models analyze continuous velocity curves, baseline latency distributions, and transaction state patterns.

When a performance degradation begins, AI tools can rapidly isolate the root cause, instantly identifying whether a delay stems from an in-store network drop, a faulty card reader or a struggling banking gateway.

More importantly, this intelligence allows for true closed-loop remediation. Integrated with intelligent payment orchestration platforms, the system can automatically reroute transaction traffic around a failing processor to an alternative rail without the customer noticing a delay. If a store loses connectivity entirely, edge architecture can switch terminals into secure, encrypted store-and-forward modes without interruption.

The high street cannot afford operational dead zones. By combining privacy-safe telemetry with AI-driven intelligence, technology leaders can finally bridge the gap between back-end infrastructure and front-of-house revenue, ensuring that when the rush hits, the checkout keeps moving.

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• Sam Altman says AI isn't creating security vulnerabilities – it's just helping us discover them far more quickly
• Attackers are also finding and exploiting them just as quickly
• AI Security Mesh helps you explore fixes at a faster pace

"AI is helping us discover all of the security issues that we have developed over the past fifty years," co-CEO Clay Magouyrk worked into a conversation on-stage with OpenAI CEO Sam Altman, clearly laboring the point that artificial intelligence is actually helping us to identify existing vulnerabilities.

This comes against a backdrop of what could only be described as an onslaught of AI-assisted bug patches – companies are now patching at rates we've never seen before. Oracle's own quarterly patch update recently addressed no fewer than around 3,000 issues, most of which were only identified thanks to AI.

With this in mind, Oracle is now helping businesses identify vulnerabilities, investigate exploits before they happen and research fixes through a new partnership with OpenAI.

AI is uncovering thousands of existing vulnerabilities

With the new Oracle AI Security Mesh feature, businesses will be able to see supporting evidence like reproduction steps and test results to help them rule out false positives and to prioritize the biggest threats.

During the conversation with Magouyrk on stage, Altman implied that businesses may eventually need to reconsider their security strategies from the ground up, moving beyond fixing individual bugs toward continuous monitoring – which could spell out potential security team restructuring as AI continues to have an impact on job roles.

OpenAI Head of Cyber Johannes Landgraf explained the threat: "Frontier AI is creating new opportunities to help organizations identify and respond to increasingly sophisticated security threats."

The biggest worry is that while AI can significantly speed up vulnerability discovery, it's also helping attackers exploit them at that same rate.

"It's maybe too hard to patch every bug one by one across the entire supply chain," Sam Altman said. "I think we'll need new ways to fix them."

For many businesses until now, one of the biggest challenges has actually been getting fixes out of the door quickly enough. The hope is that now, AI Security Mesh can equip businesses with the upper hand to respond as quickly as possible to newly discovered threats.

• AI-generated code security improved for the first time in a year, with vulnerability rates dropping from 44% to 38%
• Top models performed better at preventing common flaws like XSS, but still struggled with complex security reasoning
• Researchers stress AI-generated code still requires independent security testing and verification before deployment

Computer code generated by frontier AI models has gotten more secure and consequently - contains fewer vulnerabilities.

This is according to a new report by Veracode, which claims this is the first significant improvement in a year.

In July 2025, Veracode started releasing its GenAI Code Security Report, which analyzed code generated by LLMs for vulnerabilities and security risks. Since then, the company’s researchers noticed a trend - the code was functioning almost perfect, but on the security side it left much to be desired.

Are XSS flaws worth the trouble?

“We’ve been analyzing code generated by multiple LLMs for more than a year. Until now, we have not seen a marked improvement in AI code security, despite consistently near-perfect functionality,” said Chris Wysopal, Co-founder & Chief Security Evangelist at Veracode.

“This is the first time since running our study that we’ve been able to report a meaningful move on the security dial.”

That meaningful move is a jump from 56% to 62% in overall AI-generated code security. At the same time, there’s been a drop in vulnerabilities from 44% to 38%, Veracode added, saying this was the most significant improvement since the reports started coming out. Anthropic's Claude Opus 5.5 and OpenAI's GPT-6-Sol co-lead the rankings at 73%, surpassing the previous high of 68% set by GPT-5.5 in July 2026.

“Ten models now clear 60%, up from four in the last report, and the overall average has jumped six percentage points,” Wysopal said. “With around half of code now generated by AI, today’s results are truly encouraging.”

To draft the report, Veracode tested 18 models across four languages (Python, C#, JavaScript, and Java), and four Common Weakness Enumeration (CWE) categories (CWE-89, CWE-327, CWE-80, CWE-117). Claude Opus 5.5 and GPT-6-Sol claimed the top spot with 73% followed by GPT-5.5 which holds 68%. In July, six of 11 models scored between 50 and 53, while today only two remain in that band, while 10 scored at least 60%. The bottom is still at 44% - 50% percent.

While the results are definitely commendable, there is a caveat. Apparently, the main driver of the overall gain was on cross-site scripting (XSS), which is arguably one of the weakest categories. XSS rose to 18% which is also the single biggest change in the dataset. According to Veracode, XSS is the “easiest vulnerability to fix through pattern recognition”, which is exactly how LLMs improve. In cases where data-flow reasoning across multiple functions is required (such as, in log injection cases, for example), LLMs continue to stall.

Low-hanging fruit

In other words, AI has just picked low-hanging fruit and called it a day. We’re not sure if we should be disappointed with this, since XSS attacks are common and the vulnerability is still present across the web.

In the report, Veracode also said that water was wet and the sky is mostly blue: it found that top-performing models cost more.

However, they said throwing money at the problem most likely isn’t the best solution: “The models that produce the most secure code are the ones that cost the most to run at scale, yet they still fail on more than one in four security tasks,” Wysopal said. “The answer should never be to use the most expensive model and hope for the best. A mid-tier model at a 60% pass rate plus Veracode scanning is more accurate, more cost-effective, and more scalable than a frontier model at 73% with no verification.”

In conclusion, the improvement is there, but “mends a small part of a larger wound”, Veracode said. Organizations should make sure they combine AI code with continuous and independent security verification that can catch what the models miss.

• Researchers report the first confirmed PHP2Shell compromises, targeting organizations linked to the US government
• Attackers chained PHP2Shell with a Linux privilege-escalation flaw to gain root access and persistence
• Breaches progressed from initial compromise to Active Directory attacks within minutes, underscoring patching urgency

We now have confirmation of the first successful exploits of the infamous PHP2Shell vulnerability discovered earlier this month.

The news comes courtesy of cybersecurity outfit Dream, who claim to have seen attackers breach organizations affiliated with the US government.

PHP2Shell is a name given to two critical-severity vulnerabilities found in PHP. When they were first disclosed, most of the cybersecurity community went full panic mode, given that PHP powers roughly 70% of all websites and web apps on the internet, and there was no patch at the time. The flaws allegedly enabled complete website takeover, and some government and education organizations in Europe even preemptively shut down their websites, in anticipation of mass exploitation attempts.

But after the dust settled, a strange thing happened - there were no successful attacks. Researchers were seeing attempts and probes, but no successful breaches. Amazon said it observed first attacks four days before the patch, and Cloudflare later tracked “thousands” of attempts in the 10 hours before the patch was announced. This grew to tens of thousands within an hour of disclosure, and hundreds of thousands within two hours. Now, it’s gone into the millions.

Some said exploiting the bugs was easier said than done, and that threat actors were hitting different roadblocks, including Address Space Layout Randomization, a security feature that randomly changes where a program’s code and data are stored in memory every time it runs. Still, they warned that all of that might be temporary, and that a successful hit was a question of “when”, not “if”.

Targeting firewalls

Now it seems the claims were on point, as Dream says Chinese threat actors successfully chained PHP2Shell with a Linux kernel privilege-escalation flaw against Palo Alto Networks firewalls, breaching 25 servers, some allegedly belonging to US government-affiliated organizations.

The attacks started on October 23 2026, probing firms in the US, Japan, Taiwan, and South Korea, and soon focused on Palo Alto Networks’ PAN-OS-powered devices, running an older, vulnerable version of the OS.

According to Dream, the crooks chained PHP2Shell with Fragnesia, a local privilege escalation flaw that allows unprivileged users to obtain root access. It was disclosed in mid-May this year and is tracked as CVE-2026-46300.

After breaking into the devices, the attackers ran some 40 custom post-exploitation scripts, Dream added.

“Within hours of compromising a US government-affiliated firewall, they had tunneled into the internal network, compromised a third server using the same PHP2Shell exploit, and launched a full Active Directory attack campaign - all through a chain that started with a single unauthenticated HTTPS request to a management interface that should never have been exposed to the internet,” the company’s report reads.

Not automated

To make matters worse, the researchers believe the attackers still have unabated access.

“The total chain - from unauthenticated HTTPS connection to persistent root webshell - completes in under 15 minutes,” Dream said. The researchers could not say what the endgame was here because that information was not found in the artifacts they retrieved. Besides the obvious, which seems to be persistent access and credential theft, the threat actors could do anything from dropping backdoors to exfiltrating sensitive information.

In the context of “Black October”, and the fact we’re seeing a significant increase in automated attacks, it is also worth mentioning that this incident was likely not AI-powered.

Dream did find evidence of AI usage but believes it was not fully automated. Claude Code was installed, as was an offensive security platform called AIscan, which was configured with Chinese AI models DeepSeek and Kimi. Furthermore, they found API keys for FOFA, a Chinese counterpart to the Shodan search engine.

With the help of FOFA, the attackers allegedly identified around 19,000 internet-connected Palo Alto Networks firewalls, which they used as a starting point for their attacks.

Via The Register

• Attackers allegedly stole $6.36 billion from Binance in a record-breaking cryptocurrency exchange breach
• Investigators linked the attack to North Korea’s Lazarus Group, exploiting access from a compromised engineer
• Binance said customer funds remain safe, while authorities and partners froze billions in stolen assets

Cybercriminals, allegedly from an infamous North Korean hacking organization, have gained access to one of the biggest cryptocurrency exchanges on the planet - Binance - and in less than half an hour withdrew $6.36 billion worth of different tokens.

Security researchers TRM Labs said the attack was carried out by a group called TraderTraitor, which it describes as “North Korea’s premier hacking unit”.

“We base this assessment on robust on and off-chain intelligence sources,” TRM Labs said in a report, noting that TraderTraitor is part of Lazarus Group, North Korea’s state-sponsored threat actor blamed for some of the biggest crypto heists in history, with the FBI blaming it for the $1.5 billion Bybit hack that took place on February 21, 2025.

Gone in 20 minutes

Binance’s experts, as well as independent researchers, are still piecing together what happened, so information might change throughout the day(s). What we know so far is that a DevOps engineer had their laptop compromised, which was confirmed by Richard Teng, Binance co-CEO, in a LinkedIn post.

“That gave an attacker access to a service container, where they submitted unauthorized withdrawal transactions,” he said.

Right now, no one really knows what exactly happened and what it means that a DevOps engineer’s laptop was compromised. However, we do know that Lazarus is famous for running operations DreamJob and Contagious Interview, in which they set up fake companies and fake jobs, and then approach software engineers working on crypto projects. They entice them with lucrative job offers and exciting opportunities, and sneak malware during the multiple interview rounds.

But Binance’s security measures are more robust than that, and simply installing malware on one device could not have sufficed. Teng confirmed that, too, saying “the attacker did not simply use one engineer's permissions, and this was not a single "one-click" action. It involved a more complex attack process.”

Funds are SAFU

TRM Labs said the attackers pulled funds from 10 different chains, including Bitcoin, Ethereum, XRP, Tron, Solana, Arbitrum, Optimism, Base, Avalanche, and Polygon. The Ethereum chain was the hardest one hit, losing roughly $3.79 billion. It could be because of the chain’s smart contracts feature, which allowed the attackers to keep withdrawing funds as they were landing into the wallet.

Binance is one of the biggest and most popular cryptocurrency exchanges on planet Earth, and as such it holds billions of dollars in user funds. This attack, however, did not target user funds, but rather Binance itself. This was confirmed by both Teng and TRM Labs, with the co-CEO saying “your funds are safe”, and “Binance will cover all losses from the SAFU fund and company funds.”

In response to the attack, Binance temporarily restricted deposits and withdrawals. Numerous users took to X this morning to say their transaction requests were denied. The police and other relevant entities were notified, and around $3 billion were already frozen on-chain, apparently.

Tether, Circle, Ripple and World Liberty Financial froze about $2.67 billion in stablecoins, TRM Labs confirmed, while Arbitrum's Security Council froze 8,426 ETH and 74.6 million ARB on its network, for a total of roughly $2.7 billion.

What’s particularly interesting about this incident is how the market responded. In 2013, when Mt.Gox (at the time the biggest crypto exchange in the world) got hacked for $450 million, it was considered a “black swan” event, sending bitcoin down by more than a third (35%) in a few weeks. The 2025 Bybit hack sent BTC down approximately 7.5%. In response to the Binance hack, BTC dropped a measly 6.7%, which is just another Friday in the crypto world.

Phishing incoming

Trouble always comes in pairs, and this Binance hack will most likely be no exception. Security researchers NordVPN are warning users that other hacking groups might try to take advantage of the news to target users with phishing emails.

“Another risk is criminals exploiting the confusion and fear among crypto owners on other platforms. An email claiming your funds are at risk, a fake support account offering help or a message urging you to move your cryptocurrency to a ‘safe wallet’ could be an attempt to steal from you,” said Marijus Briedis, Chief Technology Officer at NordVPN.

“Check updates through the Binance app or a website address you already trust. Avoid links in unsolicited messages, and never give anyone your password, verification codes or wallet recovery phrase. Pressure to act immediately should be a reason to pause and verify the request.”

“These steps can reduce the risk of someone taking over your account, but they cannot prevent a breach of the platform itself. Cryptocurrency firms typically have watertight cybersecurity, as they are digital-first businesses, so if one can be involved in the biggest hack in history, it shows just how lucrative a single flaw can be.”

Digital sovereignty today is less about where data sits and more about who controls it. It is the ability for organizations to govern their data, infrastructure and operations, while retaining the flexibility needed to innovate.

Sovereignty is about having true control over your data and knowing where it lives, how it is regulated and exactly how it can be accessed at all times. Organizations can approach sovereignty through balancing regulatory requirements, operational resilience and innovation.

Different industries face vastly different pressures when it comes to data sovereignty. Banks, insurers, and healthcare providers must protect sensitive data while staying agile, while tech companies or logistics firms often require a greater level of flexibility.

In plain terms, data sovereignty is impacted by how data is governed by the laws of the country or region it is physically stored and/or accessed in. Data ownership and control are crucial, but current conversations about sovereignty have become a catch-all for resilience and security – which goes beyond data residency alone. It is never just about the physical location of the data.

The main challenge organizations face now is how to determine the right level of sovereignty for their regulatory, operational and innovation specific needs.

Sovereignty across sectors

When it comes to regulation, different industries face entirely different regulatory and operational pressures, with no single universal mandate applying across all sectors.

In highly regulated industries like healthcare, government and financial services, organizations are required to meet strict local data controls to comply with sector-specific regulations, one example being NIS Regulations within the EU. Organizations working with sensitive data must ensure they are protecting personal and financial data while they remain operationally flexible.

For example, a global bank holds highly sensitive customer data like account details and transaction history and would therefore need to operate under dense and overlapping regulatory frameworks. Both customers and regulators expect strict control over the data, requiring the organization to uphold a specific and tightly governed sovereignty framework in its foundations.

Organizations in less regulated industries like tech or retail can often leverage global cloud provider infrastructure more freely, without needing to carefully balance performance or innovation.

Sectors that don’t require high levels of sovereignty have to ensure they do not overdo the concept as it often leads to slower development and wasted resources.

Finding your place on the spectrum

Think of sovereignty as a dial which your organization uses to find your place on the spectrum. The dial moves depending on how much control you need, over what data and under which jurisdiction, to find the exact level for your organization and potentially per project or service. This method lets you examine your business in accordance to the regulatory requirements for your sector and location.

There are three main questions you need to ask to guide your decisions. Ask what your actual regulatory obligations are and where operational resilience requires local control vs where it demands global flexibility. Then think about where, in your business, does innovation depend on the free movement of data.

Once you have answered the above questions, you’ll know what level of sovereignty applies to your organization and how and when it might be subject to change. It is important to remember that your sovereignty decision must be revisited as your business scales, enters new markets and jurisdictions and as regulation evolves in your sector.

Why your database is a sovereign decision

Your sovereignty decision is essentially ensuring you understand the importance of security, compliance, accountability and explainability when it comes to your organization's internal business controls and regulatory requirements.

In light of this, database infrastructure is taking on an elevated role and supporting enterprises across industries who choose and tailor the level of sovereignty they require and that’s just right for them.

Modern database software platforms can support enterprises across the spectrum, with strict geo-fenced deployments with data localization and flexible multi-cloud database architectures for more global businesses. To become a strategic decision, sovereignty has to be considered at the data layer of your infrastructure.

Technical capabilities like end-to-end encryption, strict audit and access controls and scalable support across the globe are what often matter in a decision about sovereignty. The right model of infrastructure gives organizations the choice to tailor their level of control to their actual needs.

Sovereignty is a strategy

Sovereignty must be viewed as a spectrum that an organisation can choose from. It cannot be treated as a linear standard across industries and organizations.

Enterprises who treat sovereignty as a deliberate strategy are the ones getting sovereignty right. This outlook forces organizations to be precise about the exact thing they are protecting, at what cost and why they are protecting it.

Before asking if your organization is sovereign, think about whether you are sovereign in the ways that actually matter for your business and sector.

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Transparency duties under Article 50 have begun to apply, even as the EU AI Omnibus implementation for many high-risk systems has moved to December 2027 and, for certain AI-enabled products, to August 2028. For organizations building and deploying AI in Europe, the message is clear: regulators extended the timeline on complexity, but not on transparency.

While some organizations may view the extension as welcome breathing room, the time would be far better used preparing for the future of AI. To do that, we need to frame the challenge around creating the conditions for long-term innovation, rather than around compliance deadlines.

As AI becomes embedded in products, services and decisions that affect people's lives, organizations need confidence in the systems they are deploying, and users need confidence in the organizations deploying them. That work cannot begin in 2027. It needs to start now.

Compliance is achieved at a point in time; governance is built as a capability. A deadline can be moved, but an institutional capability cannot simply be created at short notice.

Start with the AI already in use

The starting point for every organization should be an honest assessment of whether it has a reliable picture of the tools, outputs and interactions that Article 50 covers. As we have seen in recent years, AI systems can quickly become more deeply embedded in business over time as employees explore new tools and suppliers innovate and ship new updates.

This natural expansion is a normal part of adopting a new technology, but it can result in AI becoming a core part of business processes before the proper governance is in place. Governance is most effective when it shapes those decisions from the beginning.

Attempting to retrofit governance after AI has become embedded is significantly more difficult, more expensive and ultimately less effective, and the resulting loss of trust can be more costly than the upfront work required to prevent it.

Extend the controls already in place

The good news is that many of the disciplines required by the legislation should already be familiar. They build on existing practices around privacy, security, risk management, accountability and operational governance, and much of the challenge is around applying established governance disciplines to a rapidly evolving technology landscape.

Organizations that have taken data protection regulation seriously, and already understand impact assessments and effective governance processes, are well positioned to meet many of these requirements – there is considerable overlap.

The task is to extend them for AI, not replace them. A privacy impact assessment can identify data risks, but it cannot on its own establish whether a model is dependable in a particular context, whether someone can meaningfully question its output or whether an automated process disadvantages a group.

Those questions should be built into existing workflows, particularly when AI is bought rather than built. Before a capability is embedded in software or a critical process, organizations need clarity on data treatment, service and model changes, incident response, support for transparency duties and the evidence a provider can supply. Even where the underlying model comes from a third party, organizations still need to govern how it is selected, configured and used.

The sovereignty question

Alongside the AI Act, the other crucial question impacting the way we think about governance in Europe is sovereignty. As the debate gains momentum across Europe, the practical issues raised are becoming clearer. Where is data stored? Which providers do we depend on? How resilient are our technology choices? What happens if commercial, legal or geopolitical circumstances change?

It is worth being precise about what sovereignty can realistically mean for enterprises. Full compute sovereignty, including control over chips, infrastructure and supply chains, is far beyond the reach of most organizations. The more practical goal is compute resilience: understanding where critical dependencies sit, ensuring no single vendor, cloud provider or model becomes a point of leverage, and retaining control over the layers of the AI stack that remain within the organization's influence.

Open source can support this by providing greater transparency and control over code, but it does not remove infrastructure dependency on its own. An organization may use an open model and still rely on external compute or cloud capacity to operate it.

Organizations should adopt a sovereign-by-design approach to innovation. Sovereignty should not be considered after technology decisions have been made, it needs to be embedded into decisions on data architecture, vendor selection and risk management from the outset, with resilience, not full independence, as the realistic target.

It is also important to note that not every dataset, process or workload carries the same level of risk. Organizations should take a tiered approach, applying the strongest sovereignty requirements where data sensitivity, regulatory exposure or operational importance demand it.

These are not abstract concerns. Viewing AI governance and sovereignty as interconnected challenges creates an opportunity to build a more coherent approach. This is not a case for technological isolation or for stepping back from global technology ecosystems, the aim is narrower than that. It is about ensuring organizations retain meaningful freedom of action in an increasingly complex and uncertain environment.

The real opportunity

The debate about innovation, regulation and sovereignty will continue. Europe will keep refining its approach to AI, and organizations will keep adapting to new technologies, new providers and new risks. The more important question is whether organizations use this window to build the capabilities they will need long term, rather than treating each regulatory milestone as a separate deadline.

Governance should not be viewed as a compliance exercise that slows innovation. It is an institutional capability that enables organizations to innovate sustainably, adopt new technologies with confidence, and remain resilient as markets, regulations and geopolitical conditions evolve.

The ultimate goal is innovation built on trust, and we have the ability to deliver that regardless of regulatory deadlines. The real test is not whether an organization built every part of the system it relies on, but whether it understands its dependencies well enough to change course if circumstances demand it.

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• Researcher finds exposed database containing 42 million documents and hundreds of thousands of records with personal data
• The CitySourced-linked repository exposed names, contact details, complaint reports, and internal records for years
• Database was secured after disclosure, highlighting ongoing risks from misconfigured cloud storage

Millions of documents, many exposing personally identifiable information (PII), were sitting online, in an open database accessible to anyone with a browser and the knowledge of where to look. The database belonged to a GovTech company called CitySourced, and has since been locked down.

Cybersecurity researcher Jeremiah Fowler reported finding a database contained roughly 42 million documents and 450,000 JSON files containing PII in plaintext.

The exposed records consisted primarily of images of complaints or code violation reports, but after analyzing a limited sample, Fowler found the database held people’s names, postal addresses, email addresses, phone numbers, and “thousands of internal records dating from 2016 to 2026”.

This kind of information is a gold mine for cybercriminals. By knowing private information about their targets, they can create custom, personalized phishing messages, increasing their chances of success. Data like this is frequently shared and sold on the dark web, and is often used as leverage in ransomware negotiations.

CitySourced reacts

The exposed repository contained different top-level application directories, Fowler explained, listing names such as Api, Content, Customers, Data, Developers, FileStorage, json, Product, Public, Support, Widget, temp, Logs, and others. Apparently, the archive also contained multiple .sdk and .exe files - which are quite out of place and could indicate that someone tried to plant malware.

The name of the database and files inside led Fowler to conclude they belonged to a US company called CitySourced. This is a US civic technology company (GovTech) that develops software that residents can use to report local issues such as potholes or malfunctioning streetlights to municipal authorities. It was founded in 2009 and merged with Rock Solid Technologies in 2019. Three years later, the company was acquired by government technology provider Granicus.

When reporting on these databases, Fowler usually stresses that owning and operating are two different things, and that he could not determine if a third-party contractor managed it or not. Still, after reaching out to CitySourced, the company locked the database down and thanked Fowler for his efforts:

"Thank you again for the responsible disclosure and for following up,” CitySourced said. “Granicus has reviewed the information you provided, investigated the matter internally, and is taking action to address the issue."

How to secure a database

Misconfigured databases continue to be one of the leading causes of data leaks. Some businesses don’t know they are responsible for the security of the data they create and instead believe it’s the responsibility of their cloud provider. Others are simply unaware of the misconfiguration, leaving data widely available and risking reputational damage and even class-action lawsuits.

Fowler says businesses can secure their infrastructure by segmenting customer environments and internal data.

There is no reason for all eggs to be in one basket, so in case of a compromise, configuration error, or unauthorized access involving one tenant, information belonging to other organizations remains secure.

“Storage systems, backups, and sensitive data should be encrypted and protected against unauthorized access,” he added. “Tenant isolation, access controls, and encryption provide a good first layer of defense that could limit the overall impact of a data security incident.”

Individuals should assume that cybercriminals already know something about them. There were so many breaches over the years that it’s almost impossible anyone’s data remained entirely private - at least things like names and emails. Therefore, they should always approach incoming email with skepsis, especially unsolicited ones, and those carrying links, attachments, or messages of utmost urgency.

• Kaspersky says Lazarus's Operation FlashHole, active since at least July 2025, hit at least three South Korean software, construction, and telecom equipment firms
• An updated LPEClient tool uses four ML-KEM-1024 key exchanges to protect payload delivery, a design Check Point also documented
• The post-quantum layer guards traffic against future decryption, not endpoint detection, and the most likely way in for the hackers was still phishing

North Korea's most notorious hacking group has been caught protecting its malware with the same post-quantum cryptography that governments and tech firms are still rolling out to safeguard their own data.

In its analysis of a campaign dubbed Operation FlashHole, Kaspersky's Global Research and Analysis Team (GReAT) says an updated version of LPEClient, a Lazarus Group tool, now uses ML-KEM-1024, a key exchange mechanism designed to resist attacks from future quantum computers, to secure the channel it uses to download more malware.

The findings describe a campaign active since at least July 2025 that has hit at least three South Korean organizations in the software, construction, and wired communication equipment manufacturing sectors.

A fake AI-tailored résumé, a mandatory plugin, and quantum-resistant encryption

The way in was familiar: Kaspersky found a GitHub Pages site tied to a GitHub account, created in September 2025, that the attackers used for command and control. The site first showed the résumé of a supposed senior network appliance developer, likely seen by an employee of the communications equipment maker, before the attackers swapped it in January 2026for an interior design showcase that probably targeted the construction firm.

Kaspersky assesses with medium-to-high confidence that targets received spear-phishing emails linking to these sites, which loaded JavaScript from attacker-controlled domains, though it could not determine exactly what triggered the infection.

From there, the chain ran through INISAFE CrossWeb EX, financial security software South Koreans need in order to use many online banking and government websites. How it was abused is unclear, but the malware ended up running in memory with elevated privileges, spawned by a CrossWeb EX build released in 2023 after a public advisory from South Korea's KrCERT.

That is a well-trodden route; Kaspersky says Lazarus abused CrossWeb EX in 2021, 2022, and 2023, and in Operation SyncHole, disclosed in April 2025, it used Cross EX, a related program, to compromise at least six organizations, and we also reported on the group exploiting a zero-day in MagicLine4NX, another South Korean authentication tool.

Kaspersky believes the photo on the résumé site was AI-generated, citing missing EXIF metadata and an unnatural texture, and that AI most likely built the site too, given a candidate name resembling Korea's equivalent of "John Doe" and a portfolio stuffed with filler details and unrelated companies.

ojun Ryu, senior security researcher at Kaspersky GReAT, said the findings suggest Lazarus is "bringing AI into its long-established playbook," a shift Kaspersky has also seen among other Lazarus-aligned groups. That fits a broader pattern, from Kimsuky using ChatGPT to forge a South Korean military ID to North Korean IT workers using generative AI to get hired at Western firms.

The more striking change is inside LPEClient, a profiling and payload delivery tool that first appeared in 2019. The new version no longer stores its own server address; instead, it relays traffic through ClientHub, a newly discovered loader with a variant that talks to its operators via Dropbox. It profiles installed security products and the Windows patch level, now also checks whether protections such as Shadow Stack are enabled, and encrypts that report with GOST, Russia's national cipher standard.

A security-conscious hacker group?

Then comes the post-quantum part of the equation. The server sends four ML-KEM-1024 public keys, LPEClient derives four shared secrets from them, and those are combined into a single 128-byte key for Threefish, a tweakable block cipher originally built for the Skein hash function. That key decrypts the next payload, which is loaded straight into memory.

ML-KEM-1024 is the strongest of three parameter sets in NIST's FIPS 203 standard, and the one the NSA specifies for key establishment under CNSA 2.0, its algorithm suite for US national security systems. Kaspersky's report notes, with some irony, that the group seems to have "adopted more secure post-quantum cryptography faster than many legitimate security products."

Ryu said the part that "deserves closer attention" is the group's "adoption of cryptography designed to withstand future quantum threats," which Kaspersky traces back to at least September 2024. If that holds, Lazarus was using the technology within weeks of NIST finalizing its first post-quantum standards on August 13, 2024, and more than two years before this month's disclosure.

Kaspersky is not the first to spot this, either. In August 2026, Check Point Research described a module in a separate Dream Job wave against defense companies that fetched four public keys from its server and used Kyber/ML-KEM to agree on a key to download FudModule, a rootkit that exploited the since-patched Windows zero-day CVE-2026-68820. Kaspersky identifies the module as LPEClient, and the campaign received widespread coverage at the time.

No publicly known quantum computer can yet break the classical key exchanges that protect most internet traffic, so ML-KEM only gives Lazarus an edge if someone records its traffic today and decrypts it once such a machine exists- the "harvest now, decrypt later" scenario driving the civilian migration.

For a state espionage operation whose opponents include well-funded intelligence agencies, keeping its tooling sealed against that possibility is a rational, if forward-looking, choice. Kaspersky's own reading is that the group wants to hide its later-stage payloads.

It does not, however, hide the malware where it runs; the post-quantum layer protects data in transit, and Kaspersky's analysis of the malware itself laid the whole exchange bare. Kaspersky says the work points to a dedicated developer, and the rest of the toolkit supports that view: ClientHub uses the SNOW stream cipher, a first for Lazarus according to Kaspersky, and copies headers sent by Chromium-based browsers to blend in.

Kaspersky believes the focus on software firms, the only sector hit in both SyncHole and FlashHole, reflects an appetite for source code that can be mined for new vulnerabilities. It also distinguishes FlashHole's espionage from a parallel cluster of attacks built on Lazarus's COPPERHEDGE backdoor that appears to be about making money.

For potential defenders, the lesson is the delivery rather than the cryptography: a convincing fake résumé and a trusted security plugin. Kaspersky's advice, which also promotes its own products, covers verifying unexpected links even when they lead to polished professional sites, patching quickly, removing unneeded components, restricting user privileges, and watching connections to cloud services such as GitHub and Dropbox that can double as command channels.

Since the post-quantum layer does nothing to hide what the malware does on the machine itself, capable endpoint protection software remains the realistic place to catch it. Whether its newly-found AI-assisted approach is a game changer, however, remains to be seen.

• Report says AI is being given too much authority and autonomy to handle business-critical security decisions
• 70% of organizations delegate decisions to AI that would have been made by a human 12 months ago
• A “control gap” exists between the authority given to AI and an organization’s ability to audit it

Delegating complicated decisions to AI is leading organizations to place relatively new technology in charge of user permissions, in control of sensitive data, and risk security issues, new research has claimed.

A study by Illumio looked into how organizations are handing decision recently performed by humans to AI, without apparently taking into account the potential for serious issues to arise.

It found the majority (70%) of organizations are delegating previously human-made decisions to AI, but only around half (49%) can see what action is taken by an AI agent an audit it.

AI recommendations

The nature of AI agents makes this a particularly risky strategy, and the research has found that an incredible 56% of organizations are willing to accept greater cyber risks than 12 months ago. Almost half of that group (47%) have admitted to permitting AI actions in production environments.

Allowing AI agents to have free run of enterprise data, applications, and infrastructure is leading to wholly avoidable situations. Illumio’s research found that a control gap between the rules that organizations believe AI agents are operating within and what the technology is actually capable of.

Some 76% of organizations have experienced one or more security issues thanks to AI in the past year, with 35% revealing that following AI recommendations would result in a security risk. Meanwhile, 26% reported AI agents or tools had connected to other systems without the necessary approval.

More worrying is the 24% of organizations reporting AI’s drive to access data it shouldn’t be able to see, and another 24% stating it has triggered (or produced) an unauthorized action.

Strict limits

Of course, AI is already used within cybersecurity, and has proved successful with multiple vendors rolling out AI-enhanced solutions. But while 67% of organizations are using AI cybersecurity tools, 32% let AI conduct investigations without human oversight and 29% permit the software to deploy code changes completely unsupervised.

“The biggest AI risk may not be a system that breaks its rules. It may be one that was never given meaningful limits in the first place," noted Raghu Nandakumara, VP of Industry Strategy at Illumio.

Unleashing AI agents without constraints has left businesses struggling to control the software.

As Nandakumara observes, AI agents are persistent, and attempting to outwit their next move is pointless. Instead, “businesses must set strict limits on what they can access and do, then enforce those limits within the application and across the underlying infrastructure. That is how you contain unexpected behavior before it causes real damage.”

• Attackers used SEO-poisoned GitHub pages and fake AI clients to lure victims into downloading malware
• The Yoba malware establishes persistence, evades analysis, and ultimately deploys XMRig cryptominers
• Campaign abuses interest in popular AI tools like GPT and Claude to monetize victims’ computing resources

Cybercriminals are, once again, exploiting people’s interest in AI tools to distribute cryptojackers and make money - this time around, opting for a combination of spoofed popular products, trusty old GitHub, and a little SEO poisoning.

The campaign was spotted by security experts from Check Point Research (CPR), who’ve been tracking it for the past two months, noting that the unnamed attackers created fake downloadable clients for GPT-5.6 Sol, GPT-6 Astra, and Claude Opus, and hosted them on GitHub.

Usually, when threat actors spoof legitimate software and try to disseminate it via GitHub, they go the extra mile to feign legitimacy - they artificially inflate download numbers, deploy bots to ramp up stars or forks numbers, and post fake comments. That way, when a victim opens the page, they have no reason to doubt they’re looking at legitimate, popular software.

In this case, however, they didn’t bother with GitHub. However, CPR says that the pages still appear at the very top of Google’s search engine results pages for certain keywords, indicating that they’ve invested heavily in SEO poisoning.

SEO poisoning

Search Engine Optimization (SEO) poisoning is a simple practice - criminals purchase hundreds (if not thousands) of articles, blogs, and social media posts on the dark web, and have them uploaded and published across the internet.

Each post carries a link to the desired GitHub page, giving Google the signal that that specific page is popular and relevant for certain terms.

As a result, Google pushes it to the top of its results, unknowingly aiding the attackers into reaching their targets quicker. The attackers have also created separate GitHub websites to serve as descriptions for the applications and linked them directly to downloadable installation files.

As a practice, SEO poisoning was popular even before the advent of AI, but now it’s gotten even easier, since the tools can easily generate as many articles as needed, for a fraction of the previous cost. Obviously, these texts are extremely low-quality, and Google has been working on labeling them as spam, but the process is still ongoing.

When victims land on these GitHub pages and download the executables, they think they’re getting a standalone client for an AI tool. What they’re getting instead is a piece of malware called Yoba.

Once a victim downloads and runs it, Yoba establishes persistence by creating a mechanism that would launch it automatically every time the system restarts. Average computer users might have a tough time spotting it, too, since it presents itself as legitimate Windows software.

Yoba then checks the device it’s running on to make sure it’s not in a sandbox, or infecting someone in a “friendly” country. It can deliberately delay execution for two weeks to make analysis more difficult, and checks the computer’s keyboard to avoid infecting systems from Russia, Belarus, Kazakhstan, and Uzbekistan. CPR did not say where the attackers are from, but this filter could give a strong indication.

Deploying XMRig

The malware’s final step is to contact the C2 server and pull the final payload. While this can be virtually anything - from infostealers to ransomware encryptors, CPR says it is currently being used to deploy XMRig.

XMRig is an entirely legitimate, open-source program used to mine the Monero cryptocurrency.

Unfortunately, it is extremely popular among cybercriminals because Monero is designed to be a “privacy first” coin and, unlike Bitcoin or Ethereum, cannot be as easily tracked across the blockchain. Mining requires significant computing and electricity resources, as well as for the devices to be connected to the internet. By installing XMRig to countless devices, crooks can generate a hefty amount of tokens, leaving the victims to pick up the inflated energy and compute bill.

• Attackers abused camera management software to gain initial access and establish persistence on networks
• Campaign used DLL sideloading and reverse shells to evade detection and maintain control
• Researchers urge treating camera systems as security-sensitive assets and monitoring them for abnormal activity

Security researchers have warned of an ongoing campaign targeting an apparent vulnerability in camera management software. The vulnerability is being used for initial access, allowing threat actors to establish a foothold on company infrastructure.

The warning bells were sounded by security outfit Huntress, whose researchers Harlan Carvey and Lindsey O'Donnell-Welch said that in September and October 2026, they observed a “handful of incidents” originating from camera management software.

In all cases the attackers abused a legitimate camera management system to side-load a malicious DLL file and gain access to the underlying system.

Who watches the watchmen?

Surprisingly, the team could not determine which camera software was being targeted, nor by whom.

“Huntress analysts were unable to determine the full name or creator/vendor of the software, as the file did not contain an embedded File Version Information section,” they said.

However, they did find an executable containing a few notable strings, which led them to believe a Korean company called 3R Global could be involved. They couldn’t confidently say that the Camera Management System (CMS) executable was downloaded from that company’s website, though.

Unfortunately, without knowing the exact name of the software being targeted, it is impossible to tell how many victims there were, or how big the attack surface is. Huntress’ “handful” designation does very little to explain just how dangerous this vulnerability is.

While the lack of key data limited their visibility into exactly how the threat actors were compromising the software, they could see what they were doing once they were inside - launching a reverse shell and side-loading malicious DLLs.

Once the initial access had been established, the attackers would drop a file named rs.win32.exe to the endpoint and would launch it via a command line. The command line would supply a remote IP address and port, and include an admin username.”This led us to see this as a reverse shell,” they said.

From there, they would drop a file called WpcMon.exe - a legitimate Microsoft Windows Parental Control Monitor file, used for parental-control and activity-monitoring features. In this case, however, the file is used to load malicious DLLs called IPHLPAPI.DLL and samcli.dll. On the surface, both appear to be legitimate zlib compression libraries, but in fact, they were changed to provide beaconing, contacting the external C2 server from time to time.

Huntress watches the watchmen watching the watchmen

“Part of the infection process also included creating persistence by adding a reference to the newly installed WpcMon.exe in a value beneath the user's Run key,” the researchers explained. “Threat actors typically add a program path to a user's Run registry key so Windows automatically launches their payload whenever that user signs in.”

Side-loading malicious DLL files is a popular infection technique. Instead of deploying an obviously malicious program (which might get flagged by antivirus products), the attackers drop a DLL file containing weaponized code, and then get a legitimate program (such as the parental control software) to run it. Since the process starts with a legitimate executable, it is more difficult to identify as malicious.

It does not guarantee evasion of security software, though.

Defenders should treat cameras and their management platforms as potential entry points into the broader business environment, Huntress warned. They should inventory the software, restrict its access, and monitor for unusual processes and outbound connections.

The researchers also advise defenders to use application control to prevent unapproved executables and DLLs from running in user-writeable paths, and to investigate the sequence of CMS.exe, rs.win32.exe, abnormal WpcMon.exe, and unapproved outbound connections.

• DHS data breach exposes personal details of over 41,000 employees and contractors
• The information includes personal information on ICE agents, as well as details of surveillance operations and tools used by undercover agents
• Numerous high-level officials across US government agencies have been named within the breach

The details of over 41,000 Department of Homeland Security employees and contractors has surfaced online, including staffers at Immigration and Customs Enforcement (ICE) and US Customs and Border Protection, following an apparent data breach.

The leak contains sensitive information such as email addresses, phone numbers, and office addresses for employees alongside internal information on how Homeland Security conducts undercover operations and surveillance programs.

The archive was published by a group calling itself the Department of Peace. They dubbed the leak ‘Icepick’, and shared a note stating, “They have our information, so it’s only fair that we have theirs.”

‘Icepick’ data dump exposes agents and contractors

The DHS has been locked in a battle with activists, protestors, and anonymous social media users who have been tracking and cataloguing the movements and behavior of ICE employees.

In response to protests and activist activity, the DHS has expanded its definition of “doxing” - the malicious exposure of personal information such as home addresses - to include basic information such as names.

The Icepick data leak has since been turned into a searchable database by Swiss cybersecurity researcher and journalist maia arson crimew, who said, “I don't think we should let them scare us into not looking into them.”

“The DHS especially as an agency completely lacks accountability, despite many parts of it being incredibly unpopular,” crimew said. “The people who work there know this too; for example, some of their LinkedIn bios simply say ‘stuff at US Government’.”

Of the 41,000 records exposed, around 10,000 belong to ICE employees, 6,200 belong to US Citizenship and Immigration Services, and a further 1,000 are Cybersecurity and Infrastructure Security Agency accounts.

Details on DHS operations contains references to “Keyhole” - an undercover surveillance of Minneapolis protest groups that took place earlier this year - alongside references to a Communications Intercept Center TechOps (CICTO) Program,” a “Title III Intercept Program” for wiretaps, and an “Illicit Sensor Intercept Program (ISIP).”

Tools used by employees have also been revealed in the breach, which include Palantir’s Falcon investigative platform, the PenLink platform used to analyze phone records and social media, and AI platforms such as OpenAI’s ChatGPT, Google’s Gemini, and Microsoft’s Copilot.

According to the transparency nonprofit Distributed Denial of Secrets, who verified the leak on behalf of Wired, the data was obtained from the DHS's Okta login system. An Okta spokesperson said, “We are aware of this report. Okta is secure, and we have no evidence our data or systems were compromised.”

A DHS spokesperson said that the agency is “aware of a potential breach and is actively working to address the situation swiftly and effectively. We take all security threats against our employees and officers seriously and will work to ensure all are properly protected. We will update the public when more information is available”

• FBI and international partners seized infrastructure tied to Virtualine, a hosting provider allegedly serving cybercriminals
• Authorities say the service facilitated phishing, ransomware operations, illicit crypto activity, and other crimes
• Investigators seized servers and customer data, though no arrests have been announced so far

The FBI has announced it seized the website of Virtualine, a bulletproof hosting service that allegedly allowed child pornography, laundered stolen and extorted crypto, and facilitated a wide range of other criminal activities.

The operation was carried out in partnership with German and Dutch authorities, as well as multiple private companies, with police raiding a data center in Limburg to seize “dozens of physical servers” that also hosted “hundreds” of virtual servers. As a result, they gained access to information about more than 3,000 of Virtualine’s customers, it was said.

“The police are currently taking follow-up actions towards these users,” the Dutch police said, although it’s difficult to say how valuable the information will be, since most bulletproof hosting providers don’t ask for private data, and criminals are good at hiding it, as well.

Bulletproof hosting

Every website, including hacking forums, criminal service providers, even fraudulent landing pages, needs a server that hosts it. Hosting providers renting out these servers are generally compliant with law enforcement agencies, so when the police requests a takedown or files a complaint, most of them comply. However, there is a subset of hosting providers, colloquially called “bulletproof hosting”, which are unusually resistant to pressure from the authorities.

It doesn’t mean all of them are inherently illegal or malicious. There are cases where such services are necessary - in countries with strong censorship, or where website owners want to protect their identities.

In this case, however, Virtualine was openly criminal, with the US Department of Justice (DOJ) saying it advertised its services to criminals on cybercrime forums saying, on at least one occasion, “You can use Virtualine for all illegal/black hat activities (including phis[h]ing, carding, email spam, port scanning and all others) except for child contents and terrorism activities.”

The company took it a step further. In March 2026, it introduced an “additional protection plan” that offers customers who receive takedown requests “another shell company not affiliated with Virtualine, after which the server is reactivated.”

Interestingly enough, Virtualine said it did not accept child porn or terrorism content, but according to the DOJ - did exactly the opposite.

One of its clients created a forum that allowed users to post and sell access to “nude, sexually suggestive, and/or sexually explicit images and videos of minors and adults,” while also promoting a hack-for-hire service that broke into people’s accounts and stole their explicit photos and videos. Virtualine was even notified about it by law enforcement agencies from Germany, Canada, and Australia, but decided to ignore the warnings and continue hosting the client.

At the same time, Virtualine’s customers were allowed to pay in Bitcoin, offering an unhosted bitcoin address that allowed customers “to direct pay without concern their funds would be flagged as illicit”. Citing Chainalysis data, BleepingComputer said the company’s wallet was seen receiving funds from ransomware operations, infostealers, Russian-language cybercrime forums, and more.

“Virtualine then laundered those proceeds to conceal their origin,” the blockchain company added.

Piercing the kevlar

Besides confiscating the servers and the data, the main website - virtualine.net - was defaced and now shows the usual FBI “this website has been seized” message. Unfortunately, no arrests have been made, which means the service will most likely continue operating.

According to BleepingComputer, it is “partially online” but its network prefixes currently have very low routing visibility, which could mean that much of its infrastructure was pulled offline.

Usually, disruptions such as this one are only temporary, and it won’t take long before they are remedied and the service becomes fully operational again.

• Sony Network Communications discloses breach affecting roughly 2.5 million ISP customers and former users
• Exposed data included personal details, while some email service users also lost email passwords
• Company contained the intrusion, notified authorities, and warned customers about phishing and fraud risks

A subsidiary of Sony Network Communications, the Japanese tech giant’s Internet Service Provider (ISP) arm, has confirmed suffering a data breach which saw it lose sensitive data on roughly 2.5 million people.

Among the stolen files were people’s phone numbers, email addresses, and in some cases - email passwords.

The company confirmed the news in an apology and notice letter in which it said threat actors accessed So-net, the company’s consumer-facing ISP service brand. The identity of the attackers was not disclosed, and nor was the method they used to break in. The breach happened on Monday, October 19 2026, and was spotted four days later, on October 23.

“Immediately after confirming the incident, we shut down the affected server and blocked communications from the source of the unauthorized access,” the machine-translated letter reads. A day later, the investigation determined that the attackers exfiltrated a total of 2,141,986 sensitive records belonging to both current and former members.

Trickling down to NTT Docomo

Of that number, roughly 1.8 million belong to current members, who had their names, phone numbers, email addresses, user IDs, dates of birth, genders, and internet connection IDs exposed to the hackers. Passwords were not leaked, Sony said.

There were also more than 440,000 records taken from plala (S Course), and this part also affects NTT Docomo, a separate internet service provider that uses Sony Network Communications’ underlying infrastructure as part of its service.

NTT Docomo issued a separate statement, notifying its customers of the incident, and urging them to stay vigilant with incoming emails and other communications:

“There is a risk of suspicious communications from those who misuse potentially leaked information in the future,” the machine-translated notice reads. “Please pay attention to the following points:

・Do not respond to emails, SMS, or phone calls

・Do not open links in the body

・Do not open attached files.”

The company added that the incident affects all Hikari service clients, as well as those that cancelled from May 2026, onwards.

Some So-net members’ email addresses and email passwords were, indeed, taken. A separate set of data affecting Email Service (G), A-Box (an additional mailbox service), So-net VAIO Mail, and PostPet Mail Account Service was nabbed, and the company reported 24,655 affected addresses for Email Service (G), 17,672 for A-Box, 7,005 for So-net VAIO Mail, and 5,069 for PostPet Mail Account Service. The breach affects both current So-net customers, and those that cancelled their subscriptions in March 2026 or later. Those that cancelled by the end of February were not affected, according to the announcement.

Banking data struck, too

Some customers also had their banking data exposed - but not many. “We confirmed the leakage of 16 records containing bank account information, including financial institution name, bank code, branch name, branch code, deposit account type, account number, and account holder's name,” the company said.

It stressed that it does not hold bank account PINs and therefore, they could not have been compromised. “We will individually contact the affected customers to apologize,” the notice reads.

Sony Network Communications said it notified the country’s Personal Information Protection Commission and other relevant government and law enforcement organizations and is currently investigating the attack.

So far, no hacking groups have claimed responsibility for the attack, and the data has not yet appeared anywhere on the dark web. There is also no word of this being an extortion attempt.

The attack comes less than a week after Japanese internet giant GMO Group told its customers to scan their computers for viruses, back up all of their data, disconnect the devices from the internet and then shut them down.

Via BleepingComputer

• The FEC was hit by a cyberattack on Monday that took filing systems offline
• 24 and 48 hour donation reports couldn't be filed for almost 41 hours
• The attack has been blamed on a "bad actor", but no other details have been released

Just days before voters head to the polls in what is the most significant voting occasion in Donald Trump’s second term, the Federal Election Commission went offline for almost 41 hours to contain a cyberattack.

During that time, there was a window for big donors and SuperPACs to fund campaigns without being able to submit 24 and 48 hour funding reports, likely leaving millions of dollars undeclared.

Campaign and finance watchdogs have voiced outrage about the lack of transparency into funding at a critical time in the election cycle, especially as there are no caps on how much SuperPACs can spend to help or hinder candidates.

Funding transparency goes dark

An email sent to campaign compliance firms on Monday said that FEC services were taken down after a “bad actor” attempted to gain access to systems. Outside of this, there is little information to go on regarding who would be responsible or what their motives might be.

According to OpenSecrets research director Brendan Glavin, who spoke to Reuters, around $50 million was spent per day in the lead up to the FEC outage, with $250 spent on October 20 alone.

Disclosure delayed is disclosure denied,” Hilary Braseth, executive director of nonpartisan campaign finance watchdog OpenSecrets, said in a statement during the outage. “When hundreds of millions of dollars in outside money flood political airwaves in the final days before an election, transparency is not an optional luxury, it is a fundamental public safeguard.”

As would be expected, numerous conspiracy theories surrounding the FEC outage have been floated online. Many suggested the outage would give donors a window to funnel millions of dollars of undeclared funding into candidates needing a last minute boost.

“We ​didn't have access to the last-minute spending, which is really ramped up as the election draws nearer,” Molly White, a crypto and Big Tech political spending tracker, told Reuters. “We have no visibility into that when the filing systems are down. It's pretty ⁠devastating this close to the election”

“It's hard to say with any real information that that's what happened, but it's certainly I think where people's minds go very quickly,” she concluded.

SuperPACs back pro-AI candidates

AI has been a hot topic ahead of the midterm elections, which take place on November 3. The huge buildout of AI data centers across the US - and the billions spent to fund it - have been met with opposition by the majority of Americans.

To counter the outrage and swing the vote in favor of AI, numerous SuperPACS have spent millions of dollars to support campaigns of pro-AI candidates and millions more to harm those critical of the technology.

President Trump has pushed an administrative policy of AI at any cost, claiming that the US economy and national security depends on continuing AI superiority over rivals such as China. To that end, the estimates on the total AI infrastructural spending range between $2.8 trillion by 2030 to $10.3 trillion by 2032, so there is certainly a lot riding on this election.

With many states enacting or considering moratoriums on AI data centers, anti-AI candidates could scupper the Trump administration's hopes for being an AI powerhouse.

There's no shortage of enthusiasm for artificial intelligence. Organizations across every sector are investing heavily, experimenting, launching proof of concepts, and finding ways to turn the latest developments into real business value.

However, there's a growing disconnect between experimentation and execution. For all the discussions of AI transforming the way we work, many AI projects never progress beyond the pilot stage. It's a familiar story. The proof of concept delivers promising results, the business case adds up, and everything looks like it's heading in the right direction.

Yet somewhere along the way the project hits a bump on the road. This can look like costs beginning to rise, confidence starting to waver, integration with existing legacy systems proving more complicated than expected, and approvals taking longer than anticipated.

What started out as an exciting project slowly loses its momentum. As a result, the AI initiative ends up stuck in limbo. The reason is that running a successful AI pilot and successful AI implementation are two very different things. Transitioning from a controlled environment with governed variables to a production environment brings a new set of challenges: security, governance, culture, skills, integration, and readiness.

Unfortunately, this challenge is not uncommon. Research shows that 71% of organizations remain in the pilot or proof-of-concept phase of AI transformation, highlighting just how difficult it is to turn early promise into a business reality.

This limbo is what many organizations are now experiencing: the AI implementation gap. It's the space between proving that AI can work and deploying it at scale in a way that delivers measurable value. For today's technology leaders, bridging that gap has become one of the defining challenges of AI adoption. The pressure to move beyond experimentation is growing, but doing so requires more than just increasing investment or pushing strong proof of concepts into production.

The real challenge is creating the conditions in which AI can move from an isolated trial into a sustainable part of the wider business. That requires establishing the right technical and organizational foundations, preparing people and processes, and finding the right use cases. To deliver real value, organizations need to think less about speed and more about creating something secure, scalable, and consistent.

It isn’t a technology challenge, it’s a business one

When AI projects stall, it's seldom because of the technology itself. More often, organizations run into strategic, governance, and operational challenges that prevent encouraging pilots from becoming production-ready solutions.

One of the biggest barriers is analysis paralysis. The majority of organizations have no shortage of AI ideas, but they typically lack a clear way of understanding which use cases will deliver the greatest value. This means resources become spread across multiple proof-of-concepts, each showing promise but none receiving the attention needed to reach production. The number of pilots continues to soar while the number of live deployments stagnates.

However, some organizations make the opposite mistake by designing for scale before they've checked the concept works. Major investment is made in infrastructure, architecture, and implementation before a solution has been tested in real-world conditions. If the project then needs to change direction, the amount of investment already committed can make it tricky to adapt. As a result, organizations can end up locked into an approach that no longer delivers the value they envisioned.

Governance is another common stumbling block. For organizations operating across EMEA, navigating regulations such as the EU AI Act, GDPR, and DORA introduces understandable caution. The consequences of non-compliance can be significant, and uncertainty around an evolving regulation often slows decision-making. Many AI initiatives don't stall because the business lacks ambition, but because leaders want greater confidence that they're deploying the technology in the right way: compliantly and responsibly.

Lastly, organizations often underestimate what it will cost to move from a successful pilot to widespread deployment. AI projects that appear affordable during the proof-of-concept phase can become much more expensive once they're scaled, especially if they haven't been properly stress-tested.

That's why due diligence is critical. Yet more than half (56%) of organizations don't carry out a Total Cost of Ownership assessment before deciding where to place significant workloads. Without a clear understanding of the long-term investment required, it's easy for costs to accumulate and confidence in the project to dwindle.

Balancing certainty and speed

Closing the Implementation Gap isn't about moving faster. It's about moving with confidence and know-how.

Time and time again, organizations find themselves at one of two extremes: waiting until every unknown has been resolved before making any moves or rushing ahead with AI projects before they've been properly validated. Neither approach is sustainable. Those seeing the greatest success are the organizations finding a balance between certainty and speed - moving quickly where the evidence supports it, while building in the validation and governance needed to reduce risk.

That starts with validating ideas early. Before major investment is made, organizations need to ask the technical, commercial, and governance questions that could prevent a project from ever reaching production. It's far better to discover early that a use case won't deliver than to invest months of budget and time before uncovering the same conclusion.

Rapid prototyping also plays a crucial role. Instead of debating whether an idea might work in theory, businesses should aim to create something tangible that users can test within days. Seeing a concept in action changes the conversation from speculation to evidence, giving stakeholders the confidence to make informed decisions about where to invest next.

Equally important is maintaining transparency throughout the delivery process. AI projects shouldn't feel like a black box. Defined ownership, clear checkpoints, and agreed assessment criteria help everyone to understand where a project stands, whether it’s a success, and what comes next. Projects rarely stall because the technology isn't capable - they stall because critical decisions weren't made early enough or because expectations weren't aligned from the outset.

Bridging the implementation gap

The implementation gap isn't going to disappear overnight. With 71% of organizations still stuck between pilot and production, the challenge is clearly not a lack of willingness, technical ability or ideas. In most cases, they have all three.

Instead, it's the ability to turn experiments into solutions that actually work in the real world. That means treating the transition as a business issue, not simply a technical one. Organizations need an approach that balances speed with governance, validates value before investment is made, and gives stakeholders the confidence to scale. It also requires leaders to be confident enough to stop projects in their tracks if they're not going to deliver real value.

The organizations taking meaningful steps forward won't be those adding more pilots into the mix. They'll be those that have strong processes and capabilities in place to push the right pilots ahead. Fundamentally, closing the AI implementation gap means creating a path from pilot to production that can be followed again and again.

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Since the launch of Anthropic's Mythos, the debate around the risks that AI-powered tools pose for security teams has steadily ramped up.

Yet, fundamentally, the attack targets aren’t changing: the big shift is one of scale and speed. AI finds more vulnerabilities, giving security teams more to deal with. But at the same time, AI is lowering the technical barrier to exploitation. The result is a situation where the window of time that teams have to respond has drastically reduced.

This has clear implications for how organizations approach exposure management and reinforces the need for solutions that help security and IT teams stay secure amidst this new reality.

Vulnerability detection is accelerating

The initial update from Project Glasswing underlined the power behind the Mythos model that has fueled so much speculation and concern.

In approximately one month, Mythos and around 50 partner organizations reportedly identified more than 10,000 high and critical severity vulnerabilities, with 6,202 estimated high/critical vulnerabilities found across more than 1,000 open-source projects.

There’s no doubt Mythos is hugely consequential and its capabilities mark a significant milestone, but the gap between attackers and defenders has already been widening for some time. Security teams have been facing a growing volume of CVEs; in 2025, some 48,185 CVEs were published, a 20.6% increase from the previous year and Advances in AI tools can reasonably be expected to continue fueling this trend for years to come.

This all has huge consequences for security teams. As Cloud Security Alliance analysis has reported, Mythos-type capabilities will mean the number and frequency of complex, novel attacks will rise dramatically alongside the need to patch and respond to AI-discovered vulnerabilities, exploits and autonomous attacks.

0-day vending machines, no Mythos required

Restricting access to Mythos and other highly capable models is not a practical safeguard. Our research and testing demonstrates the capabilities of general access LLMs in finding and exploiting novel vulnerabilities when being prompted by experienced security practitioners.

One of our security researchers created an automated vulnerability research pipeline, with no human in the loop. On first deployment, this found a remote, multi-stage SQL injection vulnerability sitting on 300,000 WordPress plugins, a process fully automated from discovery to exploitation. The finding, which aligned with an independent discovery by another researcher, was assigned CVE 2026 3985 and publicly disclosed.

And it’s not just the power of AI in finding vulnerabilities that should cause concern. AI-powered attacks can go a step further in automating an entire extortion attempt. An agent can autonomously create and send the exploit, then demand a ransom as we saw when an operator dubbed JadePuffer, exploited a vulnerable Langflow server, harvested credentials, moved laterally and encrypted 1,300 database records in an end-to-end agentic ransomware operation.

Reduce the attack surface as a first line of defense

This is our 'new normal' and LLM models have changed the old assumptions about the speed of vulnerability management. The window that existed between vulnerability discovery, CVEs assigned and defensive action being taken, has fundamentally shifted.

It’s not just about reaction times. Teams also need to take proactive steps to reduce their attack surface as the race to patch can be avoided entirely if assets weren’t unnecessarily exposed in the first place. This is about fighting the battle on two fronts; closing the window between vulnerability detection and remediation and taking pre-emptive action to make the target that attackers aim at smaller in the first place.

These should go hand-in-hand as many teams focus on patching what’s vulnerable, but they also need to question if it should be exposed at all. Attack surface reduction is an area that many organizations may struggle with, but any unnecessarily exposed internet facing asset is now carrying far more risk than ever.

Exposed services, such as leaving a MySQL database or private API documentation exposed to the internet, is an open invitation for attackers and these often don’t even need a CVE to be exploited. This leaves the door open for automated, high-speed attacks.

Always on exposure management

When time to exploit is reduced down to hours, the second key focus area for security teams is reducing the gap between discovery and remediation.

Finding and fixing vulnerabilities requires both context and cadence. Pentesting provides the depth and context but is a ‘point in time’ activity and frequent in-depth investigations can be cost prohibitive for many organizations. Vulnerability scanning provides the cadence but can’t interpret if what it’s found matters for your business environment.

That’s where AI can help and, whilst the traditional pentest isn’t going to disappear right away, we’re at the beginning of an era in which AI offers a new way to support the work that human security analysts and engineers are doing.

Its value lies in instantly alerting teams to new risks, the ability to deploy a pentest on every major release for the fraction of the cost of a manual engagement, agents that can actively investigate and validate findings using the same methods employed by human security experts, and integrating with the tools your teams use to get issues to the right people for fixing, fast.

This way, stretched teams can keep up with the emerging threat landscape. AI is helping teams cross that last mile - validating findings, reducing false positives, and providing necessary context.

These are still relatively early days and the frameworks around the pentest, used by auditors, insurers and compliance bodies, will need to evolve. But the signs are that this new era requires continuous testing as standard practice.

It’s needed because Mythos, and other LLM models, will punish anyone still working to yesterday's timelines. The fundamentals of reducing exposure, validating what's real, and freeing skilled people from the noise haven't changed. What's changed is how little time is left to get them right.

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• An Israeli company is alleged to have launched a spy campaign using malicious ads targeting Starlink users
• The ads do not have to be clicked on, just served to the user to begin collecting data
• The ads were served to Starlink users across more than 100 countries

An alleged Israeli spy firm has been spotted snooping on Starlink users on an industrial scale by a digital defense firm.

These are the conclusions of research by Trend Micro’s AI cybersecurity unit TrendAI, whose researchers analyzed troves of digital advertising data. The research found that Israeli company Discover-Tech was the number one bidder on Starlink advertising traffic.

The surveillance was targeted at Starlink units across more than 100 countries - including regions with Starlink restrictions - and used malicious adverts to harvest user data across browsers and applications.

Starlink users surveilled by ads

Initially, TrendAI did not directly name Discover-Tech as the perpetrator of the alleged surveillance campaign, instead referring to them as “Shadow-Earth-79”. Discover-Tech is the same firm that was alleged to have targeted Al Jazeera journalists with malicious adverts earlier this month, as exposed by Citizen Lab.

As Starlink terminals route traffic through a network of satellites in orbit, they can circumvent local internet restrictions and are a useful way to stay connected during blackouts - such as those experienced by Ukraine - or government enforced internet shutdowns - such as those that affected Iran at the start of 2026.

But this circumvention also makes them lucrative targets for governments or authorities looking to track Starlink units on their turf.

As Starlink only provides the connection to the internet, adverts can still be served on webpages, and this is the critical piece of this puzzle. As Cofiant noted in their own report, Discover-Tech abused the ad bidding process to serve adverts that harvest critical data about the users that see them. Some adverts can collect data as the user trawls the internet, creating a de-anonymized profile that can set them up for further surveillance.

The advert doesn’t even have to be clicked on. As long as it is served to the user, it can begin tracking.

The TrendAI researchers noted an “obvious skew” towards bidding on advertising space that would target Starlink users, with Discover-Tech being the number one ad-buyer across Syria, Libya, Yemen and South Sudan despite the fact that “the demand for legitimate Western advertising in sanctioned or active-conflict states is effectively zero.”

Google, Comcast, and multiple other companies have since removed Discover-Tech from their ad platforms.

Via Reuters

• Knicks employees expecting free championship rings received mandatory cybersecurity training instead
• A fake championship ring offer exposed employees to an internal phishing test
• Madison Square Garden admitted that using championship jewellery as bait was inappropriate

The New York Knicks have faced criticism after sending employees an email promising complimentary NBA championship rings that turned out to be a cybersecurity exercise.

Staff members who accepted the invitation were reportedly required to complete cybersecurity training instead of receiving jewellery commemorating the franchise's historic victory.

The incident involved workers at Madison Square Garden, where expectations of receiving championship memorabilia had already grown following the team's successful season.

Fake championship ring offer triggers mandatory training

The deceptive message circulated in late September, offering recipients personalized jewellery supposedly manufactured by the same company responsible for producing players' rings.

Employees could select between two advertised designs, Championship Gold and Garden Classic, before following a link to register their preferred choice.

However, the invitation was actually part of an internal security assessment intended to identify workers susceptible to fraudulent messages and credential theft.

Those who followed the instructions were reportedly assigned compulsory cybersecurity lessons, although the organization has not disclosed how many employees were affected.

Joe Vardon, a senior NBA journalist at The Athletic, reported that several individuals familiar with the situation verified the exercise's purpose.

The report also raised questions about whether the invitation contained recognizable warning signs that might have helped employees identify the deception.

Such exercises commonly use suspicious addresses or misleading website links, although the specific indicators included in this particular message remain unknown.

The controversy followed the Knicks' first NBA championship since 1973, making the promised jewellery particularly significant for employees celebrating the achievement.

Receiving commemorative jewellery was also a reasonable expectation because NBA organizations commonly distribute less expensive versions to personnel outside their playing squads.

Madison Square Garden acknowledges its mistake

Madison Square Garden Sports subsequently acknowledged the incident, describing its decision to exploit employees' expectations as inappropriate.

“Using the rings as a lure to gain employees’ attention was an unfortunate mistake,” it said in an official statement.

The organization explained that its security concerns stemmed from an earlier cyberattack in which employee credentials were compromised through a similar fraudulent communication.

Hackers had reportedly used those credentials to access company systems and subsequently extort the organization, prompting additional efforts to improve employee awareness.

The company also disclosed plans to distribute more than 275 commemorative items among players, coaches, employees and other individuals associated with the franchise.

According to the organization, those gifts, comprising championship rings and other jewellery, carry a combined estimated value exceeding $3.7 million.

However, the distribution arrangements have attracted attention because several assistant coaches initially received empty presentation boxes during the championship celebration.

The assistants are expected to obtain their rings later, although numerous long-time arena employees and business personnel reportedly remain excluded entirely.

The organization has not specified exactly how many workers will receive individual rings rather than alternative jewellery under its announced distribution arrangements.

Via Arstechnica

• Researchers find Bluetooth flaws in warehouse barcode scanners that could provide a foothold into enterprise networks
• Attackers could spoof scanner input, inject keystrokes, and potentially pivot to ransomware or data theft
• Kaspersky urges stronger device authentication, network segmentation, and stricter security controls for warehouse infrastructure

Cybercriminals could use barcode scanners in warehouses to break into IT infrastructure and wreak havoc, security researchers have claimed, urging organizations to take preemptive measures and secure their warehouse grounds before it’s too late.

Kaspersky researchers set out to see if there are any hidden attack vectors on warehouse infrastructure and are now claiming they found one through a barcode scanner model “from a major manufacturer”.

Opening the floodgates

In a report shared with TechRadar Pro, Kaspersky said the vulnerable device has two components: the handheld scanner, and the base station, which is then connected to a workstation, or a separate business system.

The two components communicate via Bluetooth, but apparently there are no authentication methods between the two.

So, in theory, a malicious actor could obtain the Bluetooth address of the base station through passive eavesdropping on Bluetooth traffic. Then, through the established Bluetooth connection, they could send a malicious payload to the base station which simulates keyboard strokes. The base station would “think” the keystrokes are legitimate, coming from the user of the handheld device, and forward them to the connected workstation or separate business system.

“This attack pattern was observed across devices from multiple vendors,” Kaspersky said in its report.

Through this method, a theoretical attacker could gain an initial foothold on a computer within the warehouse network. Obviously, the scanner is only the entry point and not the actual target. The target is the wider network, where malicious actors could conduct reconnaissance, exfiltrate sensitive information, or deploy malware and ransomware.

“Modern retail and logistics are profoundly digital, from enterprise-wide management platforms down to every device in a worker’s hands. This pervasive connectivity means that any technology at any tier can become an entry point for a cyberattack capable of destabilising the entire ecosystem — as our research shows, even a single barcode scanner”, said Sergey Sidorov, Head of Industrial Security Services at Kaspersky.

“The warehouse sector, and the broader retail industry, should treat cybersecurity as a top priority across every layer of their operations,”

Are other devices vulnerable, too?

Kaspersky says the same scenario can be applied to other warehouse devices like Portable Data Terminals (PDR), or thermal printers, to gain access to Warehouse Management Systems (WMS) and similar infrastructure.

Therefore, the researchers are urging companies to secure their environments by verifying every new device before it connects to the network, disabling any unused Bluetooth interfaces, enforcing strong authentication mechanisms, segmenting warehouse networks so that only essential connections are permitted, applying security updates to both devices and the WMS as soon as possible, restricting account permissions to the minimum required, and regularly conducting full‑scale attack‑path exercises.

Unfortunately, we don’t know the name of the manufacturer, or how many devices in a series could be susceptible. The researchers are saying the risk is quite large, since these devices are “widely used” in warehousing, retail, and multiple other sectors.

Global e-commerce sales are expected to reach $6.88 trillion this year, making up more than a fifth (21.1%) of total retail sales worldwide.

At the same time, according to logistics research firm Interact Analysis, there are more than 180,000 warehouses in the world, so the attack surface could be significant. But warehouses are not the only sector at risk here. Barcode scanners and related handheld terminals are also widely used in retail stores, manufacturing facilities, hospitals, pharmaceutical operations, and transportation networks.

There is no reliable way to estimate how many organizations are vulnerable, but the possibility of using an everyday operational device to gain access to a connected workstation makes the findings relevant to a broad range of industries.

• Researchers uncover critical UEFI firmware flaw that could enable remote code execution on servers
• Public release of patch code may accelerate exploit development before many vendors deploy updates
• Delayed firmware patching across hardware ecosystems leaves organizations exposed despite available fixes

Cybersecurity researchers from Anthropic and Google have discovered a critical-severity vulnerability in boot-time firmware used by practically every server in existence today.

A patch is available, but given the fragmented market of UEFI manufacturers, it could take months before most of the servers are secured. In the meantime, patch code was made publicly available, which means cybercriminals will have their work laid out for them.

Discovering the bug

Virtually all modern servers today run UEFI (Unified Extensible Firmware Interface), software built into the server’s motherboard that helps start the device and load the operating system. It includes security features like Secure Boot, which helps stop unauthorized programs from running when the machine starts.

The free, open source toolkit developers use to build this firmware is called TianoCore EDK II - it serves as a common foundation for creating UEFI firmware for different types of hardware.

Alex Matrosov of Anthropic and Dan Holmes of Google recently found that EDK II contained a critical buffer overflow vulnerability. The National Vulnerability Database (NVD) describes it as a bug in BIOS “where a user may cause “Out-of-bounds Write”, Classic Buffer Overflow, and “Incorrect Calculation of Buffer Size” by remote access.” “Successful exploitation of this flaw can result in remote arbitrary code execution, " it added.

Just like other UEFI vulnerability exploits, this one would be difficult to remediate in the wild. Deploying malicious code before security solutions, or even the operating system for that matter, means that whatever anti-malware or endpoint protection solutions are installed won’t be able to spot, or remove it. Reinstalling the operating system won’t help, and neither would replacing the hard drive.

The bug is now tracked as CVE-2026-97631, and was given a severity score of 9.5/10 (critical). Luckily enough, it doesn’t seem to have been exploited in the wild. However, that might change very soon, given that the security community botched the disclosure efforts.

Multiple companies and hardware manufacturers develop their own UEFI, and many rely on shared codebases such as EDK II. When a vulnerability is discovered in the code, fixing it upstream (in the codebase) is only half the work - each affected firmware vendor must patch their own product and then distribute the new firmware to their customers. This process can take time, leaving a window between the availability of the fix and its deployment across affected systems.

Discretion is advised

That is why discretion is paramount, and some participants seem to have dropped the ball, with Ars Technica claiming the two researchers who found the bug arranged for it to stay private until a coordinated disclosure that was planned for January 2027.

However, AMI - one of the manufacturers building motherboards - apparently published a pull request last month that not only detailed the vulnerability but also included a fix for the code.

Since the cat was out of the bag, TianoCore Project members then published a public advisory about the flaws, as well.

“With publicly available patch code, it will be trivial for advanced hackers to learn how the vulnerability is triggered and to build working exploits,” Ars Technica’s Dan Goodin writes. “Further compounding the urgency, patching UEFI code vulnerabilities in millions of end devices sold and used by thousands of hardware makers and cloud providers is cumbersome and time-consuming.”

Exploiting this flaw is not that easy, though. Threat actors need to have some level of access to the target device beforehand, meaning that the bug will be mostly used by state-sponsored threat actors, and large, highly organized hacking collectives.

It really is turning out to be Black October - especially since the PHP2Shell flaws were inadvertently disclosed to the public in similar fashion.

• AI startup Cathedral used autonomous agents to breach Anthropic systems through weaknesses in third-party software
• Agents accessed HR, sales, and IT data, exposing limited customer and partner information
• Incident highlights how AI-powered attackers can rapidly exploit vendor weaknesses despite strong internal defenses

An AI startup seemingly gave Anthropic a taste of its own medicine when it used AI agents to break into the company and expose sensitive data.

The company in question is called Cathedral, which was founded by former members of the defunct US Department of Government Efficiency (DOGE), a US government initiative established to reduce federal spending and bureaucracy, while modernizing government technology and operations.

Anthropic is running a two-fold bug bounty program through HackerOne. One part is focused on finding technical flaws in Anthropic’s systems, while the other is an invite-only program that rewards researchers for finding universal jailbreaks that bypass Claude’s safety protections.

Cathedral apparently shared its results with Anthropic through the bug bounty program. After its human researchers unsuccessfully tried breaking in for a few days, they deployed AI agents which took less than 12 hours to find a way in, by exploiting flaws in third-party business software.

Violating the rules

After accessing the company’s network, the agents accessed meeting transcripts, information on salaries and job offers, as well as Slack messages. Employee conversations with Claude were not exposed, it seems.

Anthropic confirmed the news, saying Cathedral accessed a “limited set” of sales, human resources, and IT systems, and compromised contact information and basic user account records belonging to certain customers and partners. Source code and AI model weights were not accessed.

“Our monitoring flagged some of Cathedral’s activity before they reported it to us,” Anthropic said in a statement shared with The Washington Post. “We shut off their access the same day.”

According to Gizmodo, Anthropic accused Cathedral of violating the program’s rules by failing to disclose the full extent of its activity. We don’t know if that means Anthropic won’t release the payment for the bounty, but it sure sounds like it.

Paying the bounty could be the least of Anthropic’s concerns, though. The company is currently gearing up for its Initial Public Offering (IPO), with a valuation of $1.4 billion. Leaking sensitive data to anyone, including white hat hackers, is definitely not a good signal.

A 'bunch of kids' wreaking havoc

“The fact that weaknesses in ordinary enterprise software can compromise the infrastructure of even the most advanced organizations should be a wake-up call for every company in America,” the Washington Post cited Cathedral’s report as saying.

At the same time, a senior Trump administration official allegedly told Via The Independentthat “if a bunch of kids who have just started a company can do this, then potentially the Chinese aren’t far behind.”

Cathedral can hardly be described as “a bunch of kids” - it is an AI startup focused on offensive and defensive cyber operations for the US military and government. Its founders include Gavin Kliger (former Pentagon chief data officer), Luke Farritor (best known for helping decipher ancient Roman scrolls using machine learning), Marko Elez (former SpaceX staffer), and Jack Stein (apparently worked as an investment banking associate at Salem Partners before joining DOGE).

The cybersecurity community had something to say about it. Jacob Krell, Senior Director for Secure AI Solutions & Cybersecurity at Suzu Labs, wasn’t particularly phased, saying if third-party vendors are a risk everywhere, then they’re a risk at Anthropic.

"Cathedral's agents got into Anthropic the same way attackers get into everyone else, through the vendors,” Krell said. “Security maturity is capped by the weakest vendor you integrate with, and no amount of in-house engineering changes that."

Darin Fredde, Senior Director of Technical Marketing Engineering at Ridge Security, praised Anthropic’s response: "Anthropic deserves credit for reporting that its monitoring detected some of the activity and for taking containment measures. But the real measure of remediation is independent verification that those attack paths are closed. We need to stop confusing a successful patch with a verified security outcome. Otherwise, we're just closing the front door and hoping nobody discovered the spare key."

• GMO warned customers to back up data, disconnect devices, and shut down systems after a cyberattack
• Attackers allegedly stole customer data, leaked information, and threatened ransomware deployment
• Compromised trading-platform credentials and reports of unauthorized transactions raised concerns for affected users

Japanese internet giant GMO Group has told its customers to scan their computers for viruses, back up all of their data, disconnect the devices from the internet and then shut them down.

The announcement, which literally told customers, “unplug any cords connected to your PC," was made in response to a cyberattack it recently suffered, as well as the threats then made by the malicious actors.

GMO Group gets hit

GMO Internet Group is a large Japanese technology conglomerate whose business spans internet infrastructure, cybersecurity, online advertising, financial services, and more. It brings in billions of dollars every year, and operates a broad portfolio of subsidiaries, among which is Onamae Desktop Cloud, a popular VPS service provider.

Its main customers seem to be individual retail forex (FX) traders in Japan who run automated trading software. It provides a rented Windows desktop hosted on GMO’s servers, allowing customers to keep trading applications running, while being able to shut down their own computers.

On October 20, 2026, the company disclosed having suffered a cyberattack in which threat actors installed malware on some customer environments. Apparently, up to 26,739 servers were at risk, and the company responded by progressively shutting them down, launching an investigation, and cleaning them up.

“We have confirmed that unauthorized access by a third party occurred against Onamae.com Desktop Cloud and that unauthorized programs were installed in some customers’ server environments,” the company said at the time.

The investigation determined that the attackers walked away with sensitive customer data, although it wasn’t said how many customers were compromised, or what types of information were taken. It was also later reported that the unnamed attackers were demanding roughly $50 million in ransom, in exchange for deleting the data. They threatened to release the data on the dark web if the demands aren’t met. Onamae said it wouldn’t pay the attackers, and the data eventually did leak.

Despite the data leaking, no known entities claimed responsibility for the attack.

Condolences money

The attackers also threatened to install ransomware on devices belonging to Onamae customers, triggering the warning and the call for everyone to shut down and unplug their devices:

“1. Request for a full scan of your own device for virus checks 2. Request for backup 3. Request to disconnect network and power from customer PC. Before 12:00 PM on Tuesday, October 27make sure to back up, disconnect the network, turn off the power, and unplug any cords connected to your PC,” the warning, machine-translated from Japanese, reads.

It seems the attackers never acted on their threat, since no ransomware was found on customer servers.

Onamae said it would not pay the attackers, but would instead compensate each customer $30 for their troubles, regardless or not if they actually suffered any damage or not. Those that suffered more meaningful damage will be compensated separately, it was said. Replacement computers were also offered, BleepingComputer reports.

The company has continued its investigation of the attack and is currently analyzing the leaked data to see how many customers were affected, and what is at risk. It also warned the public and the media not to share the leaked information, threatening legal information against those who do.

In one of the updates, Onamae said login credentials and some account details from users of trading platforms MetaTrader 4 and MetaTrader 5 were compromised. “Some customers have reported transactions in their trading accounts that they do not recognize,” it added, before urging customers to reset their passwords and terminate all active sessions.

The full scope of the attack, as well as material damages (which might be substantial), are yet to be seen.

Via BleepingComputer

Modern digital security frameworks were originally built to correct a flawed historical assumption: the belief that if a user or system was inside the company network, they could be trusted implicitly. Over the past decade, the rapid rise of cloud storage, subscription-based business software, and remote working destroyed that network boundary.

In response, our security models shifted focus. We began verifying the identity of the person trying to log in, inspecting the hygiene of their computer, requiring multiple verification steps during login, and continuously checking that they were who they claimed to be.

This model works well when a human is logging in to perform a task. But with the rise of autonomous AI agents, the real security risk no centralized at the login screen. It starts after access has been granted.

An AI agent does not behave like a standard piece of software. It reads a human instruction, pulls context from company databases, utilizes multiple sets of tools, chooses its own digital tools, connects to external software communication links, and triggers complex automated workflows.

Verifying the initial login simply tells you that the agent is officially recognized by your system. It tells you absolutely nothing about whether the next action the agent plans to take is safe, ethical, or correct, focus has shifted to context.

The evolution of the machine identity problem

IT teams have already experienced a simpler, quieter version of this challenge with non-human identities. For years, companies have used automated service accounts, security tokens, and digital certificates to allow different software programs to talk to one another.

Most security leaders are all too familiar with the risks these credentials present: accounts are created for a temporary project, granted permanent access to database files, and then completely forgotten long after the project has ended. They become silent, unmonitored backdoors into the network.

However, AI agents complicate this familiar problem by introducing the element of independent judgment.

A traditional service account follows strict, pre-written code. By contrast, an AI agent interprets a high-level goal, analyses real-time context, selects its own tools, and actively decides how to proceed.

As a result, security policies can no longer stop at the front door. We must protect every internal data or compute asset the agent can reach and monitor every single decision it makes.

Matching autonomy with executive control

To manage this risk, security leaders must separate two distinct concepts: how much independence an AI agent has, and what level of file access it holds.

The combination of these two factors dictates the level of control required. For example, a basic agent that merely summarizes long documents only needs limited reading rights and clear activity logs. However, an agent designed to update sensitive, production databases, access code repos, write new types of code, or alter business workflows requires mandatory human approval steps, strict operational boundaries, and a clear path to reverse any mistakes.

We can categorize these digital workers into 3 distinct levels of independence, applying tighter security controls as their autonomy increases:

1. First, there are Conversational AI Agents (GenAI), productivity agents usually built and managed by employees & teams as part of their productivity (e.g. managed agents, self hosted chat bots). When driving security, we must enable proper RBAC authorization, associated with the employee and team, ensuring access to non-sensitive or production-related data, enabling logging, and validating the response to ensure proper integrity.

2.Second, there are Agents Acting with Semi/Full Human Guardrails (AI Agents), such as customer support tools that draft emails or prepare database updates for review. Here, security teams must validate the parameters of the tools being used and require a human to approve any actions involving writing, deleting, spending money, or sending information externally. All above controls apply

3.Finally, there are Fully Autonomous Agents (Agentic AI), which reset passwords or trigger complex workflows on their own. These require the strictest boundaries, from visibility (agent architecture, tool usage, and more) and focus on active set of controls. The controls include strict rate limits, continuous real-time policy checks, network access restrictions, run time security controls (Public/Private Cloud) continuous identity and minimal authorization verification, and finally complete, unalterable audit trails.

The industry is leaning toward autonomous agents seeking human approval upon abnormal behavior, can that be triggered in your agent?

The need for real-time inspection

To enforce these policies, security teams need a dedicated vantage point within the network. They must be able to inspect the human prompt, the agent's response, the digital tools being used, and the movement of data while the work is actually happening.

This requires an architecture that can discover all active agent activity across the business, govern how those agents use internal tools, and maintain complete visibility over data flows and automated workflows.

The true value of this approach lies in context. When the behavior of an AI agent is viewed alongside network traffic, user activity, and cloud database logs, security teams can investigate incidents with clear evidence. They can instantly see which agent ran, who in the organization owns it, what data was moved, and which automated tool was called.

For business leaders, this means cleaner, more consistent governance across human users, software applications, and digital agents alike.

How to begin: Start with a single workflow

Securing this new landscape does not require a complete overhaul of your entire security department overnight.

Start by picking a single AI workflow that is currently in use or close to being launched. Map out its digital identity, its level of independence, the files it can access, the tools it uses, the points where a human must approve an action, and how you would undo a mistake.

This simple mapping exercise will quickly highlight where your security gaps lie. If you cannot see the agent's actions in real-time, approve its decisions, block its access, or explain its behavior after the fact, your security controls are simply too far away.

From there, build a baseline on how your secure your coding agents, AI harness (or AI orchestrator) up to utilizing of tools/skills/MCP and moving forward to data assets access and utilization of identity that will allow you to set the correct foundations and guard rails.

The core principle of a "never trust, always verify" security model must follow the agent into its daily work. The most meaningful decision is no longer who is logging in, but whether their next action should be allowed to happen, under these exact conditions, right now.

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