Helpful to a Fault: Measuring Illicit Assistance in Multi-Turn, Multilingual LLM Agents

arXiv:2602.16346v4 Announce Type: replace-cross Abstract: LLM-based agents execute real-world workflows via tools and memory. These affordances enable ill-intended adversaries to also use these agents to carry out complex misuse scenarios. Existing agent misuse benchmarks largely test single-prompt instructions, leaving a gap in measuring how agents end up helping with harmful or illegal tasks over multiple turns. We introduce STING (Sequential Testing of Illicit N-step Goal execution), an automated red-teaming framework that constructs a step-by-step illicit plan grounded in a benign persona

Source: arXiv cs.LG — read the full report at the original publisher.

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