Hugging Face benchmarked models on catching reward hacking and sandbox escape in agent runs
LysandreJik · x · 2026-10-30
Lysandre of Hugging Face shared that they benchmarked models on detecting dangerous agent behaviors: reward hacking, sandbox escape, harness introspection, and credential misuse.
The core challenge is catching dangerous calls without blocking legitimate work, while staying fast enough to check commands throughout an agent run. Their approach: known suspicious patterns go through rule-based checks powered by atif-scan, while calls needing more judgment go to an optional decision model — but which decision model can be trusted to make that call remains an open question.
Related event: HF Benchmark: Liquid AI LFM Tops at Detecting Dangerous Agent Behavior(2 posts)→
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