What Standard Evaluations Hide About Detectors of Hijacked LLM Agents: Rare Attacks, Unseen Tools and Benchmark Transfer
An audit of eleven LLM-agent hijack-detection benchmarks finds inflated attack shares and no tool diversity, meaning published detector accuracy may not survive deployment conditions.
Summary written by editorial AI · Source link below
arXiv:2610.41803v1 Announce Type: new Abstract: Detectors can flag hijacked large language model (LLM) agents from the traces of the tools they call. In the eleven evaluations we audited, attacks or anomalies make up 16% to 86% of the test samples, no evaluation varies that share, and six report no test on data from a second benchmark or source. We ask what such evaluations hide. On the AgentDrift and AgentDojo benchmarks we test trained detectors that read tool identities, behavior, call seque
Editorial Analysis
Enterprises relying on hijack detectors for LLM agents may be operating with false confidence if their tools were validated only on inflated benchmarks.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
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