The Network Effect of AI Secrets

The study details MAMA, a topology-aware evaluation of Personally Identifiable Information (PII) leakage in multi-agent Large Language Model (LLM) systems, demonstrating how PII-seeking messages from an attacker agent propagate across six distinct communication network topologies-with leakage quantified by the rate at which ground-truth PII entities are recovered-to reveal vulnerabilities inherent in agent-based LLM interactions.

As large language models collaborate in multi-agent systems, the risk of private information leaking increases, and new research reveals how system architecture dramatically impacts that risk.