Maosen Zhang, Jianshuo Dong, Yutong Zhang, Jie Zhang, Tianwei Zhang, Han Qiu · 2026 · arXiv (Cornell University)
DOI: 10.48550/arxiv.2610.27574 · cited by 0 works (OpenAlex) · open-access PDF
多代理系统越来越多地部署,以协调多个大语言模型(LLM)代理人。然而,这也创造了新的攻击表面:恶意代理人可能有机会利用协调。在本文中,我们提出并研究了“textit{show-up attack}”,其中恶意子代理人操纵监督者参与其分配范围以外的任务。 为了评估这种风险,我们提出了SUA-Arena,它实施了6个攻击策略,并包含了188个测试案例。 然后,我们评估了6个边界LLM,并发现它们都容易受到示范攻击。 例如,GPT-5.6-Sol作为监督者遭受了高达0.93的ASR。 缓解策略,如提示和限制通信,未能持续降低风险。 这些结果表明,多代理系统需要明确的机制来
Machine translation (Meta M2M100); the English original follows.
Multi-agent systems are increasingly deployed to coordinate multiple large language model (LLM) agents. However, this also creates new attack surfaces: malicious agents may have chances to exploit the coordination. In this paper, we propose and study the \textit{show-up attack}, in which a malicious subagent manipulates the supervisor into involving it in tasks beyond its assigned scope. To evaluate this risk, we propose SUA-Arena, which implements 6 attack strategies and contains 188 test cases. Then, we evaluate 6 frontier LLMs and find that all of them are vulnerable to show-up attacks. For example, GPT-5.6-Sol as a supervisor suffers an ASR of up to 0.93. Mitigation strategies such as prompting and restricted communication fail to reduce the risk consistently. These results show that multi-agent systems need explicit mechanisms for enforcing authority boundaries.
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Topics: Supervisor, Exploit, Computer security, Computer science, Test (biology), Key (lock)
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