AgentTrap: Measuring Runtime Trust Failures in Third-Party Agent Skills
Third-party skills are becoming the package ecosystem for LLM agents. They package natural-language instructions, helper scripts, templates, documents, and service configuration into reusable workflows. This makes skills useful, but it also introduces a new security problem: a malicious skill does not need to ask the model to perform an obviously harmful action. Instead, it can disguise the harmful behavior as part of a routine workflow, relying on the agent to execute that workflow with high-value permissions and limited human supervision. We introduce AgentTrap, a dynamic benchmark for evaluating whether LLM agents can use third-party skills while resisting malicious runtime behavior. A...
| Authors | Haomin Zhuang, Hanwen Xing, Yujun Zhou et al. |
|---|---|
| Primary category | cs.CR |
| Published | 13 May 2026 |
| Last updated at source | 13 May 2026 |
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