# Anthropic AI Model Chains Vulnerabilities Into Functional Exploits Unreleased AI model autonomously identifies and chains software flaws across major operating systems. - **Source:** [The Hacker News](https://brifing.si/source/f944ec2b-8180-47bc-94de-57085b5a6563) - **Published:** 2026-10-28 - **Topic:** [Data & AI](https://brifing.si/c/data-ai) - **Original article:** - **On Brifing:** https://brifing.si/s/a71d5cdc-9808-42db-b9bb-a548d7582d13 ## Autonomous Exploit Generation Anthropic researchers utilized an unreleased AI model to identify and chain multiple security vulnerabilities across Chrome, Safari, Windows, and Linux. The model successfully created proof-of-concept exploits in a lab environment. These findings demonstrate that AI can now perform complex security research tasks previously requiring teams of specialized human experts. ## Chrome Sandbox Escape The model chained four distinct flaws within Chrome to achieve code execution outside the browser sandbox. It corrupted memory in the V8 engine, escaped the sandbox, and exploited a race condition in CacheStorage. A final use-after-free bug in the FedCM feature allowed the model to execute arbitrary code on the host. ## Safari Policy Bypass In the Safari exploit chain, the model bypassed the same-origin policy without executing external code. By obtaining read and write access to browser memory, it modified a single value to disable security restrictions. This allowed the model to access user history and cookies, effectively compromising the browser's core data protection mechanisms. ## Linux Root Access The Linux exploit chain represents the most severe impact, granting root-level command execution over a network. The model bypassed KASLR defenses by leaking kernel memory addresses and utilized a race-condition bug to redirect program flow. The entire process took approximately 53 minutes to achieve a functional root shell on target servers. ## Windows Kernel Vulnerability The Windows chain focused on a denial-of-service attack targeting the kernel's QUIC handling. By sending specific network packets, the model could crash the machine repeatedly. While Anthropic links this to CVE-2026-33096, public records associate the flaw with HTTP.sys, highlighting discrepancies between internal research findings and existing public vulnerability documentation. ## Coordinated Disclosure Process Anthropic followed a strict coordinated disclosure policy, reporting all 62 identified flaws to vendors before public release. This allowed Google and Apple to issue patches for critical and high-severity bugs. The company emphasizes that these exploits were created in a controlled lab setting and were not observed in any real-world attacks. ## Human-AI Collaboration Limits While Anthropic claims the model required only initial prompting and minimal human involvement, the exact division of labor remains opaque. The company has not provided a detailed breakdown of human intervention steps. Furthermore, the specific internal research model used for these exploits remains unnamed, limiting independent verification of the model's autonomous capabilities. ## Strategic Security Implications This research signals a shift where AI models can autonomously prove the severity of chained vulnerabilities. By providing vetted professionals access to models like Claude Opus 5.5, Anthropic aims to assist defenders in closing identity and security gaps. The ability to verify exploits automatically changes how security teams assess their attack surface readiness. ## Sources covering this story - https://thehackernews.com/2026/10/anthropics-unreleased-ai-model-chains.html — --- _Summary written by Brifing from the original reporting. Copyright in the original article belongs to its publisher; read the full piece at the link above._