mlsanityData to 7 Oct 2026

Deny Without Disabling: Authorization-Paired Evaluation and Control for Multi-Agent Systems

arXiv 2610.00371 ↗First posted 30 Sep 2026cs.MA cs.AI cs.CRcited by 0

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Abstract (arXiv)

Multi-agent systems derive their capabilities from sharing evidence, delegating tasks, and combining information across agents. The same process creates a safety problem: contributions that are admissible in isolation can jointly enable a prohibited use. Blocking every sensitive action avoids disclosure but defeats the purpose of collaboration. We introduce authorization-paired evaluation, which makes blocking prohibited uses and completing required authorized uses a joint success criterion, and FlowReview, a framework connecting object resolution, permission ranking, and deterministic enforcement. In controlled composition experiments, reviewing combined artifacts reduces the denied-commit rate from 86.0% to zero with no loss of authorized supply. Our findings show that preserving information and lineage alone does not ensure correct permission attribution. Object identity and permission must remain connected to execution through components whose outputs can be verified. Together, these findings establish a system-level requirement for multi-agent safety: govern composed information flows while preserving the authorized capabilities that make collaboration useful.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 42 of its 60 references that are in the map. First posted on arXiv 30 Sep 2026.

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42 of 60 references.

References · 42

  1. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,193
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  11. 2024PrivacyLens: Evaluating Privacy Norm Awareness of Language Models in Actioncited by 161
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30 more
  1. 2024ST-WebAgentBench: A Benchmark for Evaluating Safety and Trustworthiness in Web Agentscited by 126
  2. 2025X-Teaming: Multi-Turn Jailbreaks and Defenses with Adaptive Multi-Agentscited by 112
  3. 2025OS-Harm: A Benchmark for Measuring Safety of Computer Use Agentscited by 104
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  6. 2025MCPSecBench: A Systematic Security Benchmark and Playground for Testing Model Context Protocolscited by 47
  7. 2026ClawsBench: Evaluating Capability and Safety of LLM Productivity Agents in Simulated Workspacescited by 31
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  12. 2026AgentLeak: A Benchmark for Internal-Channel Privacy Leakage in Multi-Agent LLM Systemscited by 20
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  16. 2024Jigsaw Puzzles: Splitting Harmful Questions to Jailbreak Large Language Modelscited by 15
  17. 2026ClawSafety: "Safe" LLMs, Unsafe Agentscited by 15
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  19. 2026A Systematic Security Evaluation of OpenClaw and Its Variantscited by 11
  20. 2026ChainCaps: Composition-Safe Tool-Using Agents via Monotonic Capability Attenuationcited by 11
  21. 2026Agents in the Wild: Safety, Society, and the Illusion of Sociality on Moltbookcited by 10
  22. 2026CI-Work: Benchmarking Contextual Integrity in Enterprise LLM Agentscited by 10
  23. 2026Quantifying Trust: Financial Risk Management for Trustworthy AI Agentscited by 8
  24. 2026Poise: Position-Aware One-Instruction Skill Injection for Silent Execution on LLM Agentscited by 7
  25. 2026Capability Gates Are Not Authorization: Confused-Deputy Failures in LLM Agent Frameworkscited by 6
  26. 2026GroupGuard: A Framework for Modeling and Defending Collusive Attacks in Multi-Agent Systemscited by 4
  27. 2026Visual Exclusivity Attacks: Automatic Multimodal Red Teaming via Agentic Planningcited by 4
  28. 2026AI Organizations are More Effective but Less Aligned than Individual Agentscited by 4
  29. 2026The Agent's First Day: Benchmarking Learning, Exploration, and Scheduling in the Workplace Scenarioscited by 3
  30. 2026Do Phone-Use Agents Respect Your Privacy?cited by 3

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