Work

Topology Matters: Measuring Memory Leakage in Multi-Agent LLMs

Year: 2026

Type: conference-paper

Abstract: Graph topology is a fundamental determinant of memory leakage in multi-agent LLM systems, yet its effects remain poorly quantified.We introduce MAMA (Multi-Agent Memory Attack), a controlled evaluation framework for comparing topology-conditioned memory leakage in multi-agent LLM systems.MAMA operates on synthetic documents containing labeled Personally Identifiable Information (PII) entities, from which we generate sanitized task instructions.We execute a two-phase protocol: Engram (seeding private information into a target agent's memory) and Resonance (multiround interaction where an attacker attempts extraction).Over 10 rounds, we measure leakage using a two-stage recovery criterion that combines exact-match extraction with LLMbased inference over the attacker's final output.We evaluate six canonical topologies (complete, circle, chain, tree, star, star-ring) across n ∈ {4, 5, 6}, attacker-target placements, and base models.Results are consistent: denser connectivity, shorter attacker-target distance, and higher target centrality increase leakage; most leakage occurs in early rounds and then plateaus; model choice shifts absolute rates but preserves broad structural trends; spatiotemporal/location attributes leak more readily than identity credentials or regulated identifiers.We distill practical guidance for system design: favor sparse or hierarchical connectivity, maximize attacker-target separation, and restrict hub/shortcut pathways via topology-aware access control.Our code is available at https: //github.com/llll121/mama-eval.

Source: Findings of the Association for Computational Linguistics: ACL (Findings of ACL)

DOI: 10.18653/v1/2026.findings-acl.1980