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Abstract

Agent skills allow LLM-based coding agents to acquire domain-specific capabilities from third-party packages, but they also introduce a new supply-chain attack surface. We present PhantomSkill, an attack framework that hides malicious behavior in a skills auxiliary resources rather than in its textual description. Its core technique, VulMask, rewrites overt malicious scripts into vulnerability-shaped implementations whose malicious behavior is activated only under attacker-controlled trigger conditions. This design shifts the visible signal from explicit malicious intent to ordinarylooking insecure code. Across representative host skills, attack goals, coding agents, generation models, and automated reviewers, VulMask preserves benign utility while reducing warning and malware-level detection compared with overt malicious scripts. Our results show that skill ecosystems require resourcelevel vetting, execution-time containment, and security policies that treat exploitable vulnerabilities in agent skills as potential malicious payloads.