English(EN) Black-Box Red-Teaming of Multi-Agent Systems via Reinforcement Learning

新框架ReMAS旨在发现多智能体AI系统的漏洞

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-23 04:00

研究人员开发了ReMAS，一个新颖的强化学习框架，用于识别多智能体系统（MAS）中的漏洞。与之前专注于单一智能体的方法不同，ReMAS专门设计用于解决MAS的复杂性，其中子智能体可能不直接与用户交互。该框架采用提示精炼和模板生成两步过程，以有效地从这些复杂系统中提取系统提示。实验表明，ReMAS的性能显著优于现有的黑盒攻击，并且在不同LLM和MAS架构上表现出适应性，凸显了针对复杂对手加强防御的必要性。 AI

影响这项研究突显了多智能体AI系统中的关键漏洞，迫切需要开发更强大的防御措施来应对复杂的对抗性攻击。

排序理由该集群描述了一篇发表在arXiv上的研究论文，详细介绍了一个用于多智能体AI系统红队测试的新框架。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

Signal score: 5 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群描述了一篇发表在arXiv上的研究论文，详细介绍了一个用于多智能体AI系统红队测试的新框架。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, safety; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: Same-day; Cluster formed today. Ranking reflects the current source set at time of score.

完整方法见我们的编辑标准。

报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Yuqi Jia, Hangsheng Zhang, Chenxi Liu, Junjie Liang, Xun Chen · 2026-10-23 04:00

Black-Box Red-Teaming of Multi-Agent Systems via Reinforcement Learning

arXiv:2610.28966v1 Announce Type: cross Abstract: Large language model (LLM) agents are increasingly deployed in multi-agent systems (MAS) to accomplish complex tasks. Prior black-box red-teaming attacks mainly focus on single agents, but we find that these methods are far less e…

本文如何被排名

报道来源 [1]

Black-Box Red-Teaming of Multi-Agent Systems via Reinforcement Learning

相关实体

相关话题