A Georgia Tech SSLab catalog of public vulnerabilities whose root cause traces to AI-written code.
https://vibesecradar.com/
We start from disclosed GHSA and CVE advisories, not from a scan of every AI commit. A finding is published only when we can show three things on the same attack path: the AI-authored change, the vulnerable behavior, and the fix that closed it. Cursor, Copilot, Claude Code, and similar tools all appear; the catalog is about the code they left behind, not a ranking of tools.
Browse the current catalog for cases and statistics. The catalog is a lower bound, not a census of every AI bug. Many AI-assisted changes never become a public advisory, and some that do leave a history we cannot recover.
How a case gets in
Match the advisory. Confirm the GHSA or CVE, the repository, the package, and the actual vulnerability — not a neighboring bug in the same project.
Find the AI change. Bind an AI authorship signal (commit trailer, co-author, agent transcript, or equivalent) to the exact commit and the hunk that matters.
Prove cause and fix. Compare the parent, the AI change, and the minimum security fix. The AI code has to affect the same mechanism the patch later closes. An AI marker on a nearby commit is not enough.
Confirm the release. Record the vulnerable and fixed versions when the advisory states them, and fold true duplicates so one GHSA is one case.
What counts: AI introduced the flaw, exposed the vulnerable path, or left a security fix incomplete.
What does not: an AI marker, git blame, or model verdict on its own. We also do not claim that AI is riskier than human code. This dataset is not a rate comparison.
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