A Saudi OpenAI engineer has been arrested for allegedly planting a hidden recording device in an executive conference room at the company's San Francisco offices to secretly record sensitive conversations. The U.S. Department of Justice announced Tuesday that Abdulrahman Alfozan, 35, who had worked at OpenAI since July 2025, was arrested on October 9 and charged with illegally intercepting oral communications. The DOJ says OpenAI security personnel discovered the recording device on September 30, plugged into a USB port behind a television in an executive conference room. According to the DOJ, OpenAI determined that the device had been concealed in toilet paper, was installed around September 28, and contained encrypted data. "On or about Sept. 30, OpenAI personnel discovered a surreptitious audio recording device (Recording Device 1) inserted into a USB port on the back of a television in an executive conference room inside one of their buildings in San Francisco," says the DOJ press release.
Unit 42 dissects a cyberattack targeting a European bank's IT infrastructure, delivering concrete defense strategies to help organizations detect and counter comparable threats. The post The Noise Inside the Network: How an Unsophisticated Attack Exposed a Third-Party Risk appeared first on Unit 42.
Today is Microsoft's October 2026 Patch Tuesday, with security updates addressing a record-breaking 1,411 flaws, including one actively exploited and two publicly disclosed zero-day vulnerabilities. This Patch Tuesday addresses 70 "Critical" vulnerabilities, 57 of which are remote code execution, 11 are elevation of privileges, and 2 are spoofing. The approximate number of bugs in each vulnerability category is listed below: When BleepingComputer reports on Patch Tuesday security updates, we only count vulnerabilities released by Microsoft on Patch Tuesday itself. Therefore, today's total excludes 348 flaws fixed earlier this month, including vulnerabilities in Azure API Center, Azure App Service, Azure Event Grid, Azure SRE Agent, Mariner, Microsoft Bookings, Microsoft Dataverse, Microsoft Exchange Server, and Microsoft Partner Center.
Microsoft released the Windows 10 KB5126657 extended security update, which includes this month's record-breaking October 2026 Patch Tuesday fixes and a few bug fixes and changes. If you are running Windows 10 Enterprise LTSC or are enrolled in the ESU program, you can install this update like normal by going into Settings, clicking on Windows Update, and manually performing a 'Check for Updates.' After installing this update, Windows 10 will be updated to build 19045.7807, and Windows 10 Enterprise LTSC 2021 will be updated to build 19044.7807. Microsoft no longer releases new features for Windows 10, and the KB5126657 update mainly includes security updates and bug fixes. The update also includes fixes released as part of today's record-breaking October 2026 Patch Tuesday, which fixed a massive 1,411 vulnerabilities across Microsoft's products, including one actively exploited zero-day flaw.
Microsoft announced today that devices running Windows 11 24H2 Home and Pro editions have reached the end of support and will no longer receive monthly security and non-security preview updates. However, according to Microsoft's lifecycle policy, Windows 11 24H2 Enterprise and Education editions remain under mainstream support until October 12, 2027. "Today, October 13, 2026, Windows 11, version 24H2 Home and Pro, and Windows 10 Enterprise LTSB 2016 have reached end of updates. The October 2026 monthly security update is the last update available for these editions," Microsoft said in a message center update. "Devices running these editions will no longer receive monthly security and non-security preview updates containing protections from the latest security threats." Microsoft advises users to upgrade to Windows 11 26H2, the latest Windows 11 version, also known as the Windows 11 2026 Update, which is now rolling out in phases to eligible Windows 11 devices where the "Get the latest updates as soon as they're available" option has been enabled.
Microsoft has released the Patch Tuesday Windows 11 KB5127648 and KB5126653 cumulative updates for versions 26H2/25H2/24H2 and 23H2 to fix security vulnerabilities and bugs, and add new features. Today's updates are mandatory as they contain the October 2026 Patch Tuesday security patches for hundreds of vulnerabilities discovered in previous months. You can install today's update by going to Start > Settings > Windows Update and clicking on 'Check for Updates.' You can also manually download and install the update from the Microsoft Update Catalog. This is the tenth 'Patch Tuesday' release in 2026, and it's based on 24H2, which means 25H2 or 26H2 get the same update. There are no exclusive or special changes. You'll get the same fixes across the two versions of Windows 11. With today's update, Microsoft is adding new emojis to Windows 11 and fixing minor issues with File Explorer.
An attacker chained three zero-day flaws in Ivanti Connect Secure, a VPN appliance, to run commands as root without logging in, security firm Volexity said on October 13. Ivanti has released fixes for two of the flaws and told customers on affected versions to "act immediately." A fix for the third is still pending, according to Volexity. Volexity, which reported the flaws to Ivanti,
GitHub has taken the step of expiring unvalidated npm trusted publishing configurations 48 hours after creation after researchers found that it was possible for an attacker to push new versions of a different user's package without having to steal their credentials. "Your configuration becomes validated and exempt from expiry after its first successful publish," the Microsoft-owned subsidiary
Ivanti urged customers on Tuesday to immediately patch an actively exploited zero-day vulnerability in the Ivanti Connect Secure (ICS) remote access gateway. Formerly known as Pulse Connect Secure and originally Juniper Pulse, the enterprise-grade ICS SSL VPN provides remote employees, contractors, and hybrid workforces with secure access to corporate and cloud resources. Tracked as CVE-2026-104935, this high-severity authentication bypass zero-day was reported by cybersecurity firm Volexity and affects ICS versions 22.8R2.4 and earlier, 25.1.2.2 and earlier, and 25.1.1.1 and earlier. Remote threat actors without privileges can exploit CVE-2026-104935 in low-complexity attacks that don't require user interaction to execute arbitrary commands on targeted ICS appliances. "At the time of disclosure, Ivanti is aware of limited exploitation in the wild. We are working directly with those organizations while making information, updates and remediation available to the broader customer community. If you are running one of the impacted versions, Ivanti recommends you act immediately," Ivanti said.
Anthropic researchers show how small AI models could autonomously replicate across IoT devices, raising concerns about adaptive, AI-powered botnets. Anthropic published a paper this week that should make anyone who manages IoT infrastructure sit down for a moment. The research shows that small AI models are approaching the capability to replicate themselves autonomously across networks of cheap, poorly secured devices, the kind of Android TV boxes that sell for fifty dollars on Amazon. The team ran a 26 billion parameter model, Gemma4-26B-A4B, distributed across 15 off-brand Android TV boxes networked together. It worked. The boxes collectively ran inference at 0.39 tokens per second, slow by any normal standard, but functional, and that’s the point. The researchers broke autonomous replication into four steps: finding devices on a network, gaining access, setting up the AI model on a new device, and repeating the process. Previous studies had already shown that small models can handle many of the hacking tasks involved. This paper looks at a different question: can a model work out how to run itself on unfamiliar hardware? That’s a key ability for an AI worm that can spread across different devices. A model with 9 billion parameters successfully set up inference on 8 of the 9 hardware types tested. Models with 30 billion parameters or fewer can already handle many of the individual steps needed for replication, although they still struggle to complete the entire process without errors. In the tests, only one model, GLM-5.2, reliably completed the full process from scratch. It spread to all 8 nodes in 4 out of 5 trials. The researchers plotted the capability trend of small open-weight models against their release dates and drew a line. “In our simplified evaluations, GLM-5.2 and stronger models are capable of end to end replication. If small models improve at their historical rate, they’ll match this capability level by the end of this year.” Anthropic states. “Small weight-based replicators may be possible soon after.” That’s the capability floor for reliable autonomous replication. The ceiling, how small the model needs to be to actually fit on the target devices, is already met by models like Qwen3.5-9B and Gemma4-26B-A4B on standard 4GB TV boxes. Right now the floor and ceiling don’t overlap. The paper’s estimate is that they will, soon. Past botnets like Mirai or Kimwolf, which infected over two million Android TV boxes at its peak, were hardcoded for specific device types and specific exploits. An AI-based replicator isn’t tied to any particular hardware or operating system. “Hard-coded botnets like the Kimwolf botnet each target a specific type of device – in Kimwolf’s case, Android TVs and mobile devices. An AI botnet isn’t tied to any particular hardware or software type, as evidenced by small models being able to set up inference engines on machines from a Jetson Nano to a Mac (see the table in section A).” states the paper. “We expect AI botnets to infect more than IoT devices, and to use weight-based replication as just one strategy in their playbook. AI botnets could also act intelligently to counter defenders, but we haven’t tested this.” This is exactly what makes it categorically harder to stop than anything a traditional worm can do. A hardcoded worm hits a wall when it encounters an unfamiliar device. A capable agent figures it out. The spread is slow, taking days rather than hours for each infection cycle. The paper compares it to a slime mold, spreading gradually across a network rather than all at once. This slower pace could make the threat harder to spot. However, the process would still leave signs, including large data transfers between devices that rarely communicate, unusual scanning activity, and continuous traffic between devices. The researchers also tested whether these models learn as they spread. Each instance keeps notes on what it tried and what failed, then passes those notes to the next copy. A Qwen3.5-9B lineage started at a 45 percent success rate on a setup task and climbed to between 61 and 84 percent across six generations. It’s not dramatic improvement, but it’s real, and it’s the kind of adaptation no traditional malware can do on its own. Giving the small models access to a more powerful advisor model didn’t help, which is an interesting finding in itself. Small models don’t know what questions to ask, so access to better answers doesn’t move the needle. The paper suggests that coordination is more likely to work the other way, a large model handling high-level decisions while small ones act as its hands across compromised devices. From the defensive perspective, the paper is clear that this threat isn’t unmanageable, at least not yet. Driving the replication rate below one new infection per existing infection, the same basic math that applies to disease epidemics, is the goal. Network anomaly detection at the ISP level caught the Mirai botnet, and the same approach would flag a weight-based replicator given the sheer volume of data it would move around. One remediation campaign against Mirai cleared 92 percent of infections within 14 days by quarantining infected subscribers and walking them through cleanup. “Quarantining infected subscribers involved restricting their networks to only being capable of reaching a short whitelist of sites, and otherwise redirecting their browsers to cleanup instructions.” state the researchers. “This cleared 92% of infections within 14 days, against 74% with no notification. We’d expect quarantining to be similarly effective for an initial defense against a weight-based replicator. Eliminating the replicator completely would mean coordinating across every ISP with an infected network, which is difficult.” Longer term, the paper points to device manufacturers and regulators as the real leverage point. Default passwords that comply with security baselines, mandatory firmware updates, and stricter IoT certification requirements all shrink the pool of targets before any worm gets a chance to scan them. The UK already made default IoT passwords non-compliant. More countries doing the same would matter. “The capabilities that make AI models generally powerful coding agents also generalize to cyber tasks, including autonomous replication. Like coding agents in 2024, we’re seeing signs of life for AI worms.” concludes the paper. “We don’t yet have a full rogue replicator, but agents are becoming increasingly capable of completing the individual steps in the replication chain (and passing on their adaptations to future copies of themselves).” Follow me on Twitter: @securityaffairs and Facebook and Mastodon Pierluigi Paganini (SecurityAffairs – hacking, botnet)
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