[Paper] Detecting Safety Violations Across Many Agent Traces
Paper Overview
Research areas: cs.AI, cs.CL Authors: Adam Stein, Davis Brown, Hamed Hassani, Mayur Naik, Eric Wong Posted: 2026-04-13 arXiv: 2604.11806Summary
Identifying safety violations requires searching across large collections of agent traces, but failures are often rare, complex, or even adversarially hidden — and only discoverable when multiple traces are analyzed together. This paper introduces Meerkat, which combines clustering with agentic search to uncover safety violations specified in natural language.By structuring the search and adaptively investigating promising regions, Meerkat discovers sparse failures without needing seed scenarios, fixed workflows, or exhaustive enumeration. In misuse, misalignment, and task-gaming scenarios, Meerkat significantly improves safety-violation detection, finding nearly 4x more reward-hacking behaviors than previous audits on CyBench.
Original Abstract
To identify safety violations, auditors often search over large sets of agent traces. This search is difficult because failures are often rare, complex, and sometimes even adversarially hidden and only detectable when multiple traces are analyzed together. These challenges arise in diverse settings such as misuse campaigns, covert sabotage, reward hacking, and prompt injection. We introduce Meerkat, which combines clustering with agentic search to uncover violations specified in natural language.*Auto-collected on 2026-04-15*