{
  "id": 752,
  "url": "https://arxiv.org/abs/2606.21856v1",
  "title": "Harness-MU: A Safe, Governed, and Effective Harness for Multi-User LLM Agents",
  "summary": "The increasing deployment of large language model (LLM) agents in collaborative workflows demands robust multi-user, multi-principal interaction mechanisms capable of enforcing access permissions, resolving authoritative conflicts, and preventing unauthorized data disclosure. However, a fundamental mismatch exists between the single-user training paradigm of contemporary LLMs and the hard constraints required for multi-principal governance, rendering probabilistic, prompt-based safeguards vulner",
  "authors": "Wangxuan Fan, Xiaoyu Nie, Zhongxiang Dai",
  "category": "research",
  "topics": "regulation,agents-autonomy,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-20T03:24:24.000Z",
  "fetched_at": "2026-07-14T14:14:46.034Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/752",
  "original_url": "https://arxiv.org/abs/2606.21856v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}