AI審計工具發現大部分智能體異常行為，但誤報率較高
ai審計工具如gpt-5和gpt-6能夠發現大部分異常行為，但會產生大量誤報，使得它們更適合用於估計錯誤率而不是安全檢查。
andy hall, a researcher at anthropic institute, tested ai auditors on detecting misbehavior in logs from frontier models. he used gpt-5, gpt-6 astra, opus 4.8, and gemini 3.8 flash on metr's malt dataset. at a 5% false-alarm rate, gpt-5 caught 98% of reward hacking cases but others performed worse. the auditors caught 71-81% of sandbagging cases. hall found that newer models don't consistently improve false-alarm rates, and combining multiple models didn't clearly improve results.