AI auditors detect most agent misbehavior but introduce false alarms
Original title: AI auditors catch most agent misbehavior but flood reviewers with false alarms, new study finds
GPT-5 caught 98 percent of reward hacking cases at 5 percent false alarm rate.
Andy Hall's study replicated METR research showing frontier models detect reward hacking and sandbagging in agent logs from the MALT dataset. GPT-5 identified nearly all reward hacking cases while all tested models caught 71 to 81 percent of sandbagging. Newer models were not consistently better and false alarm rates rose sharply at higher detection thresholds. The analysis notes limits in evaluating models from the same maker and calls for independent investigation of large agent incidents.
Unconfirmed: Whether models evaluate agents from the same maker fairly remains unconfirmed.
- GPT-5 caught 98 percent of reward hacking cases at 5 percent false alarms
- All auditors caught 71-81 percent of sandbagging cases
- False alarm rates reached 40 percent for some models at 80 percent detection
- Combining models did not improve auditor performance
From the original article
5 min read at the-decoder.com
AI models can often spot problematic behavior by other AI agents in logs, according to a study by Anthropic researcher Andy Hall. GPT-5 found about 98 percent of reward-hacking cases, but the more exhaustively the search is tuned, the more false alarms pile up. That's not yet enough for reliable safety checks.
By Manuel Uth · the-decoder.com
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