提出多智能体系统安全框架，通过角色与结构探索提升防御能力
MASTER: Multi-Agent Security Through Exploration of Roles and Topological Structures -- A Comprehensive Framework
- 基于角色与拓扑结构自动构建多智能体系统
- 利用信息流机制实现攻击任务动态分配，破坏力显著
- 适合研究多智能体安全与防御的学者与工程师
基于大语言模型的多智能体系统（MAS）因角色分工与协作交互，在多个领域展现出强大的问题求解与任务规划能力。然而，这也加剧了在多智能体攻击下的安全风险。为此，本文提出MASTER框架，聚焦不同场景下多样化的角色配置与拓扑结构。该框架支持自动化构建多种MAS配置，并采用基于信息流的交互范式。为应对复杂场景中的安全挑战，设计了一种情境自适应、可扩展的攻击策略，利用角色与拓扑信息动态分配针对性、领域特定的攻击任务，实现智能体协同执行。实验表明，该攻击方法在多数模型上均表现出显著破坏性。同时，提出相应的防御策略，显著提升了多智能体系统在多样化场景下的鲁棒性。本研究期望为未来多智能体系统安全研究提供重要参考。
原文摘要 · Abstract (English)
Large Language Models (LLMs)-based Multi-Agent Systems (MAS) exhibit remarkable problem-solving and task planning capabilities across diverse domains due to their specialized agentic roles and collaborative interactions. However, this also amplifies the severity of security risks under MAS attacks. To address this, we introduce MASTER, a novel security research framework for MAS, focusing on diverse Role configurations and Topological structures across various scenarios. MASTER offers an automated construction process for different MAS setups and an information-flow-based interaction paradigm. To tackle MAS security challenges in varied scenarios, we design a scenario-adaptive, extensible attack strategy utilizing role and topological information, which dynamically allocates targeted, domain-specific attack tasks for collaborative agent execution. Our experiments demonstrate that such an attack, leveraging role and topological information, exhibits significant destructive potential across most models. Additionally, we propose corresponding defense strategies, substantially enhancing MAS resilience across diverse scenarios. We anticipate that our framework and findings will provide valuable insights for future research into MAS security challenges.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。