SkillSieve: A Hierarchical Triage Framework for Detecting Malicious AI Agent Skills

Agent skills extend local AI agents, such as Claude Code and OpenClaw, with additional functionality. Their growing popularity has led to dedicated marketplaces resembling mobile app stores, as well as automated scanners that assess whether skills are benign or malicious. However, scanner reports from individual marketplaces classify up to 46.8% of skills as malicious, raising concerns about false positives. We present the largest empirical security analysis of the AI agent skill ecosystem to date. We collect 238,180 unique skills from three major distribution platforms and GitHub, and analyze their contents, behavior, and repository context. Unlike existing scanner-based assessments, which evaluate skills largely in isolation, our repository-aware analysis checks whether a flagged skill is consistent with its surrounding GitHub project. This context substantially reduces the number of suspicious skills: only 0.52% remain suspicious after repository-aware analysis. Our results show that existing scanners can substantially overestimate maliciousness when repository context is ignored. At the same time, we identify previously undocumented real-world attack vectors, including the hijacking of skills hosted in abandoned GitHub repositories. Overall, our findings provide a more robust view of the agent-skill ecosystem's current risk surface and highlight the need for context-aware security evaluation.

Skills extend an agent's capabilities by injecting instructions and information into the context, and are widely used by agents such as OpenClaw and Claude Code. Prior work shows third-party marketplaces host malicious skills that give attackers direct influence over the victim's agent. The emerging defense scans skills before installation, pairing deterministic static checks with an LLM-based semantic judge, as in NVIDIA's SkillSpector. We show that such defenses fall to an attacker who knows the detector. Our white-box LLM attacker, Pretext, iteratively crafts skills that evade detection while still delivering the payload and performing the benign task: moving the payload from code into natural language leaves static analysis inert, while framing it as the skill's legitimate purpose and splitting instructions across files keeps the LLM stage below its blocking threshold. Across three open-source models, Pretext achieves up to 97% and 77% against a frozen detector and a co-adaptive one, respectively, revealing major gaps in current skill scanners.

Agent skills turn natural-language instructions into actions, creating a security challenge: legitimate automation and malicious behaviour can look deceptively similar. A practical screen must catch nearly every attack while keeping the review load manageable. We present SkillScreen, a compact decision model trained to score skill maliciousness directly from SKILL$.$md in a single forward pass. On MaliciousSkillBench, SkillScreen flags 20-25% of benign skills at 99% recall on unseen attack families, substantially fewer than the word-level classifier and prompted LLM judges tested. Under the same training protocol, it also generalizes better than a fully fine-tuned encoder to unseen sources. Yet unfamiliar benign sources sharply increase false positives, showing that recognizing new attacks does not ensure reliable screening of new skill collections. Our results demonstrate the value of task-trained decision models for high-recall skill screening. Project page: https://jerroldh.github.io/SkillScreen-webpage/