Somebody is finally counting the CVEs

Hanqing Zhao and the Systems Software & Security Lab at Georgia Tech have been running a project called Vibe Security Radar since May 2025: take public vulnerability advisories, trace the fix backwards to the commit that introduced the bug, and check whether an AI coding tool left fingerprints on it.

The trend line is the story. Six CVEs attributed to AI-generated code in January 2026, fifteen in February, thirty-five in March. Seventy-four confirmed cases in total across roughly fifty tools tracked. The researchers reckon the real number is five to ten times what they can detect — four to seven hundred across open source — because most AI-written code does not announce itself.

One finding needs handling with care, and the coverage mostly did not handle it carefully. Claude Code accounts for the largest share of attributed flaws, and the researchers say that is partly because it leaves identifiable signatures. That is a detection artefact, not a quality ranking. A tool that stamps its commits is a tool you can measure; a tool that does not is a tool that vanishes from your dataset. If you take that chart as a league table of which assistant writes the least safe code, you have read it exactly backwards — you are looking at a map of which vendors are legible to researchers.

The genuinely alarming number is buried in the middle: Claude Code alone touched more than four percent of public GitHub commits last month. Whatever the per-commit defect rate turns out to be, it is now multiplied by a denominator that did not exist two years ago.

Methodology this transparent is rare in this corner of the discourse. Most "AI code is insecure" statistics you will see quoted this year come from vendors selling scanners. This one comes from a lab publishing its method, and it is correspondingly less dramatic and more useful.