发表机构
Tulane University; University of British Columbia; Virginia Tech（杜兰大学; 不列颠哥伦比亚大学; 弗吉尼亚理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对多智能体协作中的安全风险，提出授权配对评估与FlowReview框架，将拒绝提交率从86.0%降至零且不损失授权供给。
AI 中文摘要
多智能体系统的能力源于跨智能体共享证据、委派任务和整合信息。同一过程也带来了安全问题：单独可接受的贡献可能共同促成被禁止的用途。阻止每个敏感操作可以避免泄露，但会破坏协作的目的。我们引入了授权配对评估，将阻止被禁止的用途和完成所需的授权用途作为联合成功标准，并提出了FlowReview框架，该框架连接了对象解析、权限排序和确定性执行。在受控组合实验中，审查组合工件将拒绝提交率从86.0%降至零，且授权供给无损失。我们的研究结果表明，仅保留信息和谱系并不能确保正确的权限归属。对象身份和权限必须通过输出可验证的组件与执行保持连接。总之，这些发现确立了多智能体安全的系统级要求：在治理组合信息流的同时，保留使协作有用的授权能力。
英文摘要
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.
Comments44 pages, 9 figures. Code and data: https://github.com/yunbeizhang/FlowReview
