@inproceedings{liu-etal-2026-topology,
title = "Topology Matters: Measuring Memory Leakage in Multi-Agent {LLM}s",
author = "Liu, Jinbo and
Cao, Defu and
Wei, Yifei and
Su, Tianyao and
Liang, Yuan and
Dong, Yushun and
Liu, Yan and
Zhao, Yue and
Hu, Xiyang",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1980/",
pages = "39728--39746",
ISBN = "979-8-89176-395-1",
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 (multi-round interaction where an attacker attempts extraction). Over 10 rounds, we measure leakage using a two-stage recovery criterion that combines exact-match extraction with LLM-based inference over the attacker{'}s final output. We evaluate six canonical topologies (complete, circle, chain, tree, star, star-ring) across $n\in\{4,5,6\}$, 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."
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<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 (multi-round interaction where an attacker attempts extraction). Over 10 rounds, we measure leakage using a two-stage recovery criterion that combines exact-match extraction with LLM-based inference over the attacker’s final output. We evaluate six canonical topologies (complete, circle, chain, tree, star, star-ring) across nın{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.</abstract>
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%0 Conference Proceedings
%T Topology Matters: Measuring Memory Leakage in Multi-Agent LLMs
%A Liu, Jinbo
%A Cao, Defu
%A Wei, Yifei
%A Su, Tianyao
%A Liang, Yuan
%A Dong, Yushun
%A Liu, Yan
%A Zhao, Yue
%A Hu, Xiyang
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F liu-etal-2026-topology
%X 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 (multi-round interaction where an attacker attempts extraction). Over 10 rounds, we measure leakage using a two-stage recovery criterion that combines exact-match extraction with LLM-based inference over the attacker’s final output. We evaluate six canonical topologies (complete, circle, chain, tree, star, star-ring) across nın{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.
%U https://aclanthology.org/2026.findings-acl.1980/
%P 39728-39746Markdown (Informal)
[Topology Matters: Measuring Memory Leakage in Multi-Agent LLMs](https://aclanthology.org/2026.findings-acl.1980/) (Liu et al., Findings 2026)
ACL
- Jinbo Liu, Defu Cao, Yifei Wei, Tianyao Su, Yuan Liang, Yushun Dong, Yan Liu, Yue Zhao, and Xiyang Hu. 2026. Topology Matters: Measuring Memory Leakage in Multi-Agent LLMs. In Findings of the Association for Computational Linguistics: ACL 2026, pages 39728–39746, San Diego, California, United States. Association for Computational Linguistics.