AI RESEARCH
When Safe Skills Collide: Measuring Compositional Risk in Agent Skill Ecosystems
arXiv CS.AI
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ArXi:2606.00448v1 Announce Type: cross LLM agents increasingly rely on community-contributed skills that expand an agent's operational capability set. We study a core safety problem in agentic AI systems: whether individually safe skills can compose into unsafe installed skill sets. We present SkillReact, a compositional security measurement framework with three components: a deterministic static-composition benchmark, a two-rater LLM-assisted human-adjudication pipeline, and an action-based exploitability harness.