LLM agents often fail when safety-critical facts are missing from tool responses—even when all returned information is true—and their ability to recover missing evidence varies significantly across models.
This paper introduces EpiSEM-Bench, a benchmark for testing how well AI agents handle missing safety-critical information when using tools. The benchmark contains 300 task pairs where key facts are systematically hidden to see if agents will perform unsafe actions. It evaluates whether agents can detect missing information, recover it, and make appropriate decisions.