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

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ethics.ai (10 August 2026), “ColluSkill: Adversarial Cross-Skill Composition for Evading Agent Skill Scanners,” evidence record 18262, https://ethics.ai/record/18262 (originally published by arXiv cs.AI).

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