用分层数据管理保护大模型多智能体系统安全
AgentSafe: Safeguarding Large Language Model-based Multi-agent Systems via Hierarchical Data Management
- 按安全等级分类信息，限制敏感数据访问权限
- 对抗攻击下防御成功率超80%，有效防伪造和恶意注入
- 适合关注多智能体系统安全的开发者与研究者
基于大语言模型的多智能体系统正推动自主通信与协作的发展，但面临未经授权访问和数据泄露等安全威胁。为此，我们提出AgentSafe框架，通过分层信息管理和内存保护提升MAS安全性。该框架将信息按安全级别分类，仅授权智能体可访问敏感数据。其包含两个组件：ThreatSieve通过验证信息权限防止伪装攻击；HierarCache是一种自适应内存管理机制，首次系统性防御代理内存的未授权访问与恶意污染。在多种LLM上的实验表明，AgentSafe显著增强系统韧性，在对抗条件下防御成功率超过80%。同时具备良好可扩展性，随智能体数量和信息复杂度增加仍保持稳定性能。结果证明AgentSafe在保障MAS安全方面的有效性及其实际应用潜力。
原文摘要 · Abstract (English)
Large Language Model based multi-agent systems are revolutionizing autonomous communication and collaboration, yet they remain vulnerable to security threats like unauthorized access and data breaches. To address this, we introduce AgentSafe, a novel framework that enhances MAS security through hierarchical information management and memory protection. AgentSafe classifies information by security levels, restricting sensitive data access to authorized agents. AgentSafe incorporates two components: ThreatSieve, which secures communication by verifying information authority and preventing impersonation, and HierarCache, an adaptive memory management system that defends against unauthorized access and malicious poisoning, representing the first systematic defense for agent memory. Experiments across various LLMs show that AgentSafe significantly boosts system resilience, achieving defense success rates above 80% under adversarial conditions. Additionally, AgentSafe demonstrates scalability, maintaining robust performance as agent numbers and information complexity grow. Results underscore effectiveness of AgentSafe in securing MAS and its potential for real-world application.
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