{
  "id": 16635,
  "url": "https://arxiv.org/abs/2608.04018",
  "title": "Governing Execution Risk in Agentic AI Systems: A Trajectory-Guided Framework for Red Teaming",
  "summary": "arXiv:2608.04018v1 Announce Type: new Abstract: AI agents are increasingly embedded in organizational workflows, where they interact with external information sources and invoke digital tools to perform operational tasks. As organizations adopt such systems, a critical challenge is identifying and mitigating risks arising from malicious or untrusted external information that can steer agents toward unintended actions. Existing red-teaming approaches largely rely on fixed attack templates or fina",
  "authors": "Zhihao Zhu, Yi Yang",
  "category": "research",
  "topics": "safety-alignment,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-06T04:00:00.000Z",
  "fetched_at": "2026-08-06T05:10:11.148Z",
  "source_slug": "arxiv-cscy",
  "source_name": "arXiv cs.CY",
  "source_homepage": "https://arxiv.org/list/cs.CY/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/16635",
  "original_url": "https://arxiv.org/abs/2608.04018",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}