Show HN: I benchmarked LLM agents on fixing real-world security vulnerabilities
A researcher created 'CVE-Bench' to test how well LLM agents can fix real-world security vulnerabilities. The study found that while agents can produce plausible patches, they often fail to address the full scope of a vulnerability, highlighting a critical bottleneck in automated security.
Why it matters
It demonstrates the current limitations of AI in cybersecurity and the danger of relying on automated tools for critical software maintenance.
The agent edited the right file, passed every regression test, and confidently said the bug was fixed. But it wasn’t. The vulnerability was still there: a different branch of the same logic, untouched. Without the sharp eyes of a security researcher, the agent’s plausible-but-incomplete patch would ship undetected. This is the most operationally dangerous failure mode I found, and it showed up repeatedly across models and tasks.
The article presents technical findings and methodology without ideological bias.
Get smarter about the news
Sign up free for a feed built around what you actually care about, Dive Deeper research on any story, and the full text of every article.
Create free accountAlready have an account? Sign in