Large Language Model-based Multi-Agent Systems (LLM-MASs) have demonstrated remarkable real-world capabilities, effectively collaborating to complete complex tasks. While these systems are designed with safety mechanisms, such as rejecting harmful instructions through alignment, their security remains largely unexplored. This gap leaves LLM-MASs vulnerable to targeted disruptions. In this paper, we introduce Contagious Recursive Blocking Attacks (Corba), a novel and simple yet highly effective attack that disrupts interactions between agents within an LLM-MAS. Corba leverages two key properties: its contagious nature allows it to propagate across arbitrary network topologies, while its recursive property enables sustained depletion of computational resources. Notably, these blocking attacks often involve seemingly benign instructions, making them particularly challenging to mitigate using conventional alignment methods. We evaluate Corba on two widely-used LLM-MASs, namely, AutoGen and Camel across various topologies and commercial models. Additionally, we conduct more extensive experiments in open-ended interactive LLM-MASs, demonstrating the effectiveness of Corba in complex topology structures and open-source models. Our code is available at: https://github.com/zhrli324/Corba.

本研究针对大语言模型驱动的多智能体系统（LLM-MAS）在安全性方面的研究缺口，提出了一种新颖的攻击方法——传染性递归阻塞攻击（Corba）。该方法通过利用阻塞攻击的传染性和递归特性，能有效干扰智能体之间的交互，并在复杂网络结构中表现出高度的破坏性。实验结果表明，Corba能在多种拓扑结构和开源模型中持续耗尽计算资源，具有显著影响力。

CORBA：基于大语言模型的多智能体系统中的传染性递归阻塞攻击