SkillSafetyBench: Evaluating Agent Safety under Skill-Facing Attack Surfaces
SkillSafetyBench benchmarks LLM agent safety against attacks through reusable skill modules, exposing how tool access and execution environments widen the attack surface.
Summary written by editorial AI · Source link below
arXiv:2605.12015v3 Announce Type: replace Abstract: Reusable skills are becoming a common interface for extending large language model agents, packaging procedural guidance with access to files, tools, memory, and execution environments. However, this modularity introduces attack surfaces that are largely missed by existing safety evaluations: even when the user request is benign, unsafe influence may reside in skill guidance, local artifacts, or execution-environment files that steer the agent
Editorial Analysis
Modular skill plugins in LLM agents create overlooked attack vectors; standardised benchmarks like this help enterprises quantify the risk before deployment.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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