How can I secure my LLM multi-agent system?
G-Safeguard: A Topology-Guided Security Lens and Treatment on LLM-based Multi-agent Systems
This paper introduces G-Safeguard, a security framework for LLM-based multi-agent systems (MAS). It aims to detect and mitigate the spread of malicious information or adversarial attacks within a group of interacting AI agents.
G-Safeguard leverages a graph neural network (GNN) to analyze the communication patterns between agents and identify potentially compromised individuals. It then uses "topological intervention," primarily by pruning connections between agents, to prevent the spread of harmful information. Key features relevant to LLM-based MAS are its topology-aware approach, considering the network of interactions, and its inductive transferability, allowing application to MAS of varying sizes and LLM backbones without retraining. Experiments demonstrate G-Safeguard's effectiveness in detecting and mitigating prompt injection, tool attacks, and memory poisoning in diverse MAS configurations.