When Safe Skills Collide: Measuring Compositional Risk in Agent Skill Ecosystems
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. On 1,520 ClawHub skills, 651 pass individual inspection and form 211,575 pairs; the benchmark flags 22.25% of these as structural candidates. We treat this raw rate as a recall-oriented scanner ceiling and calibrate...
| Authors | Su Wang, Pin Qian, Yihang Chen et al. |
|---|---|
| Primary category | cs.SE |
| Published | 30 May 2026 |
| Last updated at source | 30 May 2026 |
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