AI BriefingMeasuring the Impact of LLMs on N-day Vulnerability Exploitation
Key point
LLMs are automating and accelerating N-day attacks on disclosed vulnerabilities, raising security threats.
Details
A significant portion of existing cybersecurity threats stems from N-day vulnerabilities—vulnerabilities that have already been disclosed but remain unpatched. Attackers find these vulnerabilities through patch diffing, comparing code before and after a patch, a process that has historically required substantial time from skilled experts.
Recent research shows that LLMs are removing this bottleneck in vulnerability analysis and exploit development. Anthropic's latest model, Claude Mythos Preview, achieved the following results:
- Autonomously generated 8 working code-execution exploits out of 18 Firefox security patches
- Generated 8 exploit chains that escalate a low-privileged user to SYSTEM privileges out of 21 Windows kernel patches
These results suggest that even general-purpose models could generate exploits if the safeguards of security-focused models are removed. Therefore, companies and developers need to speed up patch application to minimize the patch gap (the window between patch release and application).
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