ColluSkill: Adversarial Cross-Skill Composition for Evading Agent Skill Scanners
Agent skills are emerging as an important attack surface in LLM-based agent systems. Through an empirical study of existing skill scanners, we find that current defenses mainly inspect individual skills, leaving risks from cross-skill composition insufficiently examined. This creates a practical blind spot: multiple locally plausible skills may pass security checks while collectively forming a harmful workflow during agent execution. To investigate this threat, we propose ColluSkill, a collusive multi-skill-chain attack framework that decomposes a complete malicious intent into interdependent sub-payloads embedded in independently packaged skills. The attack does not rely on any single ma...
| Authors | Puyu Zeng, Simeng Qin, Jingzhi Li et al. |
|---|---|
| Primary category | cs.CR |
| Published | 10 August 2026 |
| Last updated at source | 10 August 2026 |
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