Under the Hood of SKILL.md: Semantic Supply-chain Attacks on AI Agent Skill Registry

• URL: http://arxiv.org/abs/2605.11418v1
• Date: Tue, 12 May 2026 02:11:54 GMT
• Title: Under the Hood of SKILL.md: Semantic Supply-chain Attacks on AI Agent Skill Registry
• Abstract summary: We study SKILL.md - only attacks across three registry-facing stages of the Agent Skill lifecycle.<n>We show that SKILL.md is not passive documentation but operational text that shapes which third-party capabilities agents find, trust, and use.
• Score: 49.83108591873481
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Autonomous AI agents increasingly extend their capabilities through Agent Skills: modular filesystem packages whose SKILL.md files describe when and how agents should use them. While this design enables scalable, on-demand capability expansion, it also introduces a semantic supply-chain risk in which natural-language metadata and instructions can affect which skills are admitted, surfaced, selected, and loaded. We study SKILL.md - only attacks across three registry-facing stages of the Agent Skill lifecycle, using real ClawHub skills and realistic registry mechanisms. In Discovery, short textual triggers can manipulate embedding-based retrieval and improve adversarial skill visibility, achieving up to 86% pairwise win rate and 80% Top-10 placement. In Selection, description-only framing biases agents toward functionally equivalent adversarial variants, which are selected in 77.6% of paired trials on average. In Governance, semantic evasion strategies cause malicious skills to avoid a blocking verdict in 36.5%-100% of cases. Overall, our results show that SKILL.md is not passive documentation but operational text that shapes which third-party capabilities agents find, trust, and use.

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