PACE: Provenance-Aware Capability Enforcement for Tool-Using LLM Agents
Tool-using large language model (LLM) agents turn generated text into real side effects, so poisoned tool metadata, retrieved pages, memory, and reusable skills can steer the next call. Vetting an artifact before admission does not settle this. A safe variant and a leaking variant can produce the same admission evidence, and a sound gate then cannot relax that site for either. We make that condition precise, which leaves the last boundary a deployment can still act on. We present Provenance-Aware Capability Enforcement (PACE), which mediates every tool call immediately before it executes. Path confinement proposes an executable cut of represented influence paths, while capability and effe...
| Authors | Fengpeng Li, Qizhou Wang, Yuke Hu et al. |
|---|---|
| Primary category | cs.CR |
| Published | 1 October 2026 |
| Last updated at source | 1 October 2026 |
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