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)
GitLab fixed a critical vulnerability with a maximum CVSS score of 10.0 that could let unauthenticated attackers run code on its servers. GitLab has addressed a critical security flaw, tracked as CVE-2026-97229 (CVSS score of 10.0), that could let attackers run code on its servers without logging in. The flaw affects Community Edition (CE) and Enterprise Edition (EE), it is an integer overflow bug caused by weak validation of regular expressions in CI/CD configurations. The issue affects GitLab versions 19.2 before 19.2.8, 19.3 before 19.3.5, and 19.4 before 19.4.2. Users should upgrade to a patched version to protect their systems. GitLab advisory “GitLab has remediated an issue that, under certain conditions, could have allowed an unauthenticated user to execute arbitrary code on the GitLab server via a specially crafted regular expression in a CI/CD configuration, due to insufficient regular expression validation.” reads the advisory. The researchers joaxcar reported this vulnerability through the company’s HackerOne bug bounty program. The company also addressed these flaws: CVE-2026-92883 (CVSS score: 8.7) – A security flaw where an attacker can trick another user’s web browser into running malicious JavaScript code through a specially designed repository. CVE-2026-16301 (CVSS score: 8.5) – An authorization flaw that lets a user with a Developer role bypass security permissions and run automated testing/deployment (CI/CD) pipelines on protected code branches. CVE-2026-14814 (CVSS score: 8.2) – A pattern-matching flaw that allows an attacker to manipulate cached files, causing target users’ automated pipelines to run malicious code and potentially leak sensitive environment variables. In early October, GitLab released patches for a critical vulnerability in its AI Gateway, tracked as CVE-2026-90970 (CVSS score of 9.9), that could allow an authenticated user with access to the Duo Agent Platform to execute arbitrary commands on the gateway. GitLab disclosed the flaw on October 2 and fixed it with the release of AI Gateway versions 19.2.4, 19.3.2 and 19.4.1. The issue affects the way the AI Gateway handles custom flow prompt templates. According to GitLab, a user with Duo Agent Platform access could use a specially crafted flow configuration to escape the prompt template sandbox and execute commands on the AI Gateway host. “GitLab has remediated an issue in the GitLab AI Gateway that, under certain conditions, could have allowed an authenticated user with Duo Agent Platform access to escape the prompt template sandbox via a specially crafted flow configuration, leading to arbitrary command execution on the AI Gateway.” reads the advisory. The key point is that the attack does not require an unauthenticated connection. GitLab’s advisory does not provide further details about the exact conditions required to exploit the flaw or the permissions needed beyond that access. GitLab credited the HackerOne researcher invisiblemeerkat with responsibly reporting the vulnerability. Follow me on Twitter: @securityaffairs and Facebook and Mastodon Pierluigi Paganini (SecurityAffairs – hacking, CVE-2026-97229)
The Norwegian Data Protection Authority (DPA) has fined Xandr, an advertising unit owned by Microsoft, €8.6 million ($9.62 million) for illegally relaying Grindr users' sensitive data to advertisers. The administrative penalty is the largest issued by the data protection watchdog, arguing the case merited a high fine because of the "very serious" nature of the breach. The infringements
The dangerous part of a stolen identity isn't always the initial login. It's what an attacker can do with that access next. With AI accelerating reconnaissance, exploitation of vulnerabilities, and privilege escalation, attackers can move through systems faster than security teams can investigate. A compromised account that looks legitimate at first could become the starting point for a much
A user opens a browser tab, pastes data into a chat assistant, and nothing is installed. A developer adds a coding assistant as an editor extension, and nothing shows up in the installed programs list. Someone downloads a local model runtime, a program that runs models directly on the machine instead of sending data to an external service, because they were told not to send company data
Attackers took remote control of fridges and freezers at hundreds of French supermarkets on the night of October 11 and warmed them, forcing stores to throw food away, several retail chains said. The attackers reached them through the controller that runs all of a store's fridges and freezers, a device made by Danish company Danfoss that maintenance firms can manage remotely. Les Mousquetaires
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