@inproceedings{shi-etal-2026-saiguard,
title = "{SAIG}uard: Communication-State Simulation for Proactive Defense of {LLM} Multi-Agent Systems",
author = "Shi, Ruxue and
Wang, Yili and
Du, Mengnan and
Zhang, Qinggang and
Miao, Rui and
Liu, Yixin and
Wang, Xin",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.623/",
pages = "13095--13112",
ISBN = "979-8-89176-462-0",
abstract = "LLM-based multi-agent systems (MAS) solve complex tasks through inter-agent collaboration, but their communication-driven nature also allows security risks to spread across agents and trigger system-wide failures. Existing MAS defenses mainly follow a reactive paradigm after execution by detecting and isolating harmful agents, which may cause irreversible damage and degrade collaborative utility. To address this, we propose a proactive defense framework for MAS security, namely a Simulation-aware Interception Guard (SAIGuard). SAIGuard performs communication-state simulation over the MAS interaction graph, estimates the impact of incoming messages on local agent states and the global MAS state, and detects risky messages via reconstruction deviations from benign communication patterns. Instead of isolating agents, SAIGuard sanitizes or regenerates suspicious messages before it propagation into system. Experiments across diverse topologies and attack scenarios show that SAIGuard reduces attack success rates while maintaining MAS utility, outperforming reactive defenses."
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%0 Conference Proceedings
%T SAIGuard: Communication-State Simulation for Proactive Defense of LLM Multi-Agent Systems
%A Shi, Ruxue
%A Wang, Yili
%A Du, Mengnan
%A Zhang, Qinggang
%A Miao, Rui
%A Liu, Yixin
%A Wang, Xin
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F shi-etal-2026-saiguard
%X LLM-based multi-agent systems (MAS) solve complex tasks through inter-agent collaboration, but their communication-driven nature also allows security risks to spread across agents and trigger system-wide failures. Existing MAS defenses mainly follow a reactive paradigm after execution by detecting and isolating harmful agents, which may cause irreversible damage and degrade collaborative utility. To address this, we propose a proactive defense framework for MAS security, namely a Simulation-aware Interception Guard (SAIGuard). SAIGuard performs communication-state simulation over the MAS interaction graph, estimates the impact of incoming messages on local agent states and the global MAS state, and detects risky messages via reconstruction deviations from benign communication patterns. Instead of isolating agents, SAIGuard sanitizes or regenerates suspicious messages before it propagation into system. Experiments across diverse topologies and attack scenarios show that SAIGuard reduces attack success rates while maintaining MAS utility, outperforming reactive defenses.
%U https://aclanthology.org/2026.findings-emnlp.623/
%P 13095-13112
Markdown (Informal)
[SAIGuard: Communication-State Simulation for Proactive Defense of LLM Multi-Agent Systems](https://aclanthology.org/2026.findings-emnlp.623/) (Shi et al., Findings 2026)
ACL