{
  "id": 5134,
  "url": "https://arxiv.org/abs/2607.01236v1",
  "title": "Safeguarding LLM Agents from Misalignment through Provenance Analysis",
  "summary": "As LLM agents gain increasing access to powerful tools, ensuring that their actions are aligned with the user's intent becomes critical. When an agent's proposed tool invocation deviates from the user's intent -- a phenomenon called misalignment -- it may lead to harmful consequences that are difficult to undo. Existing runtime guardrails rely on an LLM-as-a-judge paradigm that lacks a systematic framework for reasoning about alignment, often producing judgments that are inconsistent or difficul",
  "authors": "Yining She, Yiliang Liang, Eunsuk Kang",
  "category": "research",
  "topics": "safety-alignment,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-01T03:16:35.000Z",
  "fetched_at": "2026-07-14T16:31:31.212Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/5134",
  "original_url": "https://arxiv.org/abs/2607.01236v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}