Memory Poisoning Propagation and Repair Mechanism in Multi-Agent Collaborative Environments

Multi-agent systems often rely on long-term memory or shared knowledge bases to enhance collaborativeefficiency, yet this introduces risks of memory poisoning and cross-agent propagation. Addressing the covertdiffusion of poisoned information during collaboration, this study proposes a memory poisoning detection andrepair method tailored for multi-agent environments.This approach constructs an evidence graph based onmemory source credibility and content consistency to validate newly added memories. It combines contrastivelearning models to identify anomalous memories exhibiting command-induced characteristics. Upon detectingpoisoning, further propagation is suppressed through isolation, rewriting, and conflict resolution. Experimentsevaluated the method using 60 collaborative tasks, approximately 210,000 memory records, and 12,000injected poisoned samples.Results demonstrate an AUC of 0.94 in poisoning detection, reducing misbehaviorrates from 15.6% to 2.3% while decreasing cross-agent propagation by 78.1% on average, with minimal impacton overall task efficiency.