Benchmarking Security Risk Detection and Verification in Open Agentic Skill Ecosystems
Open agent platforms allow community contributors to publish reusable skills that agents can invoke at runtime. This extensibility also creates a supply-chain risk: malicious contributors can hide harmful behavior inside skills that appear benign under superficial inspection. However, existing defenses are hard to evaluate because there is no benchmark that measures both malicious-skill detection and runtime verification. We present SkillVetBench, a two-stage security vetting benchmark for open agentic skill ecosystems. The first stage performs semantic vetting over each skill's natural-language specification to detect hidden malicious intent. The second stage executes flagged skills in a...
| Authors | Ismail Hossain, Sai Puppala, Zhuoran Lu et al. |
|---|---|
| Primary category | cs.CR |
| Published | 30 May 2026 |
| Last updated at source | 30 May 2026 |
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