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English(EN) AgentLeak: A Benchmark for Internal-Channel Privacy Leakage in Multi-Agent LLM Systems

新基准揭示多智能体LLM系统存在重大隐私风险

作者 PulseAugur 编辑部 · [1 个来源] ·

一项名为AgentLeak的新基准测试已被开发出来，用于评估多智能体大型语言模型（LLM）系统中的隐私风险。与仅检查最终输出的先前基准测试不同，AgentLeak分析了智能体之间的内部通信通道，例如智能体间消息和共享内存。使用此基准测试对七个隐私相关路径和1000个场景进行的评估显示，虽然多智能体配置可以减少最终输出中的泄露，但它们引入了显著的内部通道泄露，其中智能体间消息是一个主要关注点。该研究强调，仅关注输出的标准防御措施不足以保护多智能体LLM系统。 AI

影响 强调了多智能体LLM系统需要超越输出监控的新安全措施。

排序理由 该集群包含一篇介绍LLM系统评估新基准的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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新基准揭示多智能体LLM系统存在重大隐私风险

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该集群包含一篇介绍LLM系统评估新基准的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Faouzi El Yagoubi, Godwin Badu-Marfo, Ranwa Al Mallah ·

    AgentLeak：多智能体LLM系统中内部通道隐私泄露的基准测试

    arXiv:2602.11510v3 Announce Type: replace Abstract: Multi-agent Large Language Model (LLM) systems create privacy risks that current output-only benchmarks cannot measure. When agents coordinate on tasks, sensitive data may pass through inter-agent messages, shared memory, and to…

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