{
  "id": 11339930,
  "title": "PACE: Provenance-Aware Capability Enforcement for Tool-Using LLM Agents",
  "url": "https://urgent.news/2026/10/01/pace-provenance-aware-capability-enforcement-for-tool-using-llm-agents",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-01T09:18:05.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2610.01349v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "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…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}