[Paper] Detecting Safety Violations Across Many Agent Traces

Summary

This paper addresses the significant challenge of detecting complex AI safety violations that are often rare, hidden, or only visible when analyzing multiple agent traces together. Traditional per-trace methods struggle to identify these failures, which are critical in scenarios like misuse campaigns, reward hacking, and prompt injection. The research aims to improve the auditing of AI systems by enabling the detection of these difficult-to-spot safety issues.

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