Harness-MU: A Safe, Governed, and Effective Harness for Multi-User LLM Agents
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
Record details
Published: 20 June 2026
Source: arXiv
Category: Research
Topics: Regulation · Agents & autonomy · Transparency
Retrieved: 14 July 2026
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How to cite this record
ethics.ai (20 June 2026), “Harness-MU: A Safe, Governed, and Effective Harness for Multi-User LLM Agents,” evidence record 752, https://ethics.ai/record/752 (originally published by arXiv).
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