mlsanityData to 7 Oct 2026

AuthorityLens: Rethinking LLM-Based Agent Systems Through the Lens of Authority

arXiv 2609.32378 ↗First posted 26 Sep 2026cs.AIcited by 0

On the map: LLM reasoning → Reasoning for agents, planning and code

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Abstract (arXiv)

LLM-based agents are increasingly deployed with authority over consequential resources and decisions in real systems. These agents often operate alongside human and LLM-based participants who hold different forms of authority. Yet workflow roles, permission settings, and review mechanisms do not necessarily reflect the authority realized in practice. We introduce AuthorityLens, a framework for measuring a system's authority structure. Starting from an authority portfolio, we evaluate a system along three dimensions: what the system is authorized to do (System Authority), how much joint participation is required to exercise that authority (Authority Separation), and how much authority each participant holds (Principal Authority). We derive these measurements from the minimal combinations of participants sufficient to realize each outcome across admissible runtime states. We apply AuthorityLens to Codex, OpenCode, and Gemini CLI across 13 operating configurations over a common portfolio of agent operations. We find that nominal configurations do not map cleanly onto realized authority. In Codex, Full Access changes System Authority only marginally while substantially concentrating authority in the executing Assistant. OpenCode's Build and Plan configurations have the same System Authority and Authority Separation despite different workflows and root-level permissions. In Gemini CLI, model-based review increases Authority Separation without changing System Authority. Principal Authority further distinguishes authority replication from authority separation: spawned or delegated agents can become alternative holders of the same authority without increasing the required joint participation. Together, these results demonstrate that AuthorityLens provides a unified framework for measuring and comparing realized authority structures across agent systems.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning. Builds on 12 of its 52 references that are in the map. First posted on arXiv 26 Sep 2026.

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12 of 52 references.

References · 12

  1. 2025Why Do Multi-Agent LLM Systems Fail?cited by 559
  2. 2025Defeating Prompt Injections by Designcited by 256
  3. 2024Defending Against Indirect Prompt Injection Attacks With Spotlightingcited by 233
  4. 2024Magentic-One: A Generalist Multi-Agent System for Solving Complex Taskscited by 221
  5. 2025Progent: Securing AI Agents with Privilege Controlcited by 121
  6. 2025OS-Harm: A Benchmark for Measuring Safety of Computer Use Agentscited by 104
  7. 2026Taming Various Privilege Escalation in LLM-Based Agent Systems: A Mandatory Access Control Frameworkcited by 50
  8. 2025We Urgently Need Privilege Management in MCP: A Measurement of API Usage in MCP Ecosystemscited by 15
  9. 2026When Lower Privileges Suffice: Investigating Over-Privileged Tool Selection in LLM Agentscited by 12
  10. 2026Capability Gates Are Not Authorization: Confused-Deputy Failures in LLM Agent Frameworkscited by 6
  11. 2025MiniScope: A Least Privilege Framework for Authorizing Tool Calling Agentscited by 1
  12. 2026Isolation as a First-Class Principle for LLM-Agent System Safety: Concepts, Taxonomy, Challenges and Future Directionscited by 1

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  1. 2026Beyond Agent Harnesses: Cross-Substrate Authority for Multi-Agent Systems
  2. 2026When Memory Becomes Authority: Benchmarking Authority Collapse at the Memory Consolidation Boundary
  3. 2026Reframing LLM Agent Security as an Agent-Human Interaction Problem
  4. 2026When Tool Outputs Become Commands: Separating Action Induction from Runtime Authorization in Tool-Augmented LLM Agents
  5. 2026Organizational Control Layer: Governance Infrastructure at the Execution Boundary of LLM Agent Systems
  6. 2026Loop-Back Authority in LLM Agent Teams: A Paired Experiment on Flat and Hierarchical Coordination
  7. 2026Delegation Without Trust: An Empirical Gap Analysis of Identity, Authorization, and Runtime Governance in Multi-Agent LLM Systems
  8. 2026Maat: Independent Deterministic Contract-Based Governance for Multi-Agent LLM Workflows