STARS: Skill-Triggered Audit for Request-Conditioned Invocation Safety in Agent Systems
Autonomous language-model agents increasingly rely on installable skills and tools to complete user tasks. Static skill auditing can expose capability surface before deployment, but it cannot determine whether a particular invocation is unsafe under the current user request and runtime context. We therefore study skill invocation auditing as a continuous-risk estimation problem: given a user request, candidate skill, and runtime context, predict a score that supports ranking and triage before a hard intervention is applied. We introduce STARS, which combines a static capability prior, a request-conditioned invocation risk model, and a calibrated risk-fusion policy. To evaluate this settin...
| Authors | Guijia Zhang, Shu Yang, Xilin Gong et al. |
|---|---|
| Primary category | cs.AI |
| Published | 11 April 2026 |
| Last updated at source | 11 April 2026 |
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