@inproceedings{huang-etal-2026-toposhield,
title = "{T}opo{SHIELD}: Reshaping the Flow of Malice via Spatio-Temporal Risk-Aware Topological Evolution in Multi-Agent Systems",
author = "Huang, Ruiyang and
Wang, Chenxi and
Zhang, Tinghe and
Liu, Fengrui and
Sun, Jiayan and
Wang, Haocheng and
Wu, Yifan",
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.426/",
pages = "8740--8768",
ISBN = "979-8-89176-395-1",
abstract = "While LLM-based Multi-Agent Systems (MAS) demonstrate remarkable problem-solving capabilities, their interconnectivity acts as a conduit for the rapid spread of malicious injections. Addressing the limitations of static defenses, we present TopoSHIELD, a framework that reshapes the flow of malice via risk-aware topological evolution. Our approach utilizes a spatio-temporal graph neural network to monitor interaction dynamics, calculating node risk entropy (NRE) and edge attack conductivity (EAC) to pinpoint vulnerabilities. Guided by these metrics, TopoSHIELD executes precise structural interventions, pruning high-risk edges and isolating compromised communities to block attack diffusion. Empirically, TopoSHIELD reduces toxicity by 58{\%} on GPT-4o while preserving high utility ({\ensuremath{>}}90{\%} success rate), outperforming existing baselines in both suppression efficiency and scalability."
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<abstract>While LLM-based Multi-Agent Systems (MAS) demonstrate remarkable problem-solving capabilities, their interconnectivity acts as a conduit for the rapid spread of malicious injections. Addressing the limitations of static defenses, we present TopoSHIELD, a framework that reshapes the flow of malice via risk-aware topological evolution. Our approach utilizes a spatio-temporal graph neural network to monitor interaction dynamics, calculating node risk entropy (NRE) and edge attack conductivity (EAC) to pinpoint vulnerabilities. Guided by these metrics, TopoSHIELD executes precise structural interventions, pruning high-risk edges and isolating compromised communities to block attack diffusion. Empirically, TopoSHIELD reduces toxicity by 58% on GPT-4o while preserving high utility (\ensuremath>90% success rate), outperforming existing baselines in both suppression efficiency and scalability.</abstract>
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%0 Conference Proceedings
%T TopoSHIELD: Reshaping the Flow of Malice via Spatio-Temporal Risk-Aware Topological Evolution in Multi-Agent Systems
%A Huang, Ruiyang
%A Wang, Chenxi
%A Zhang, Tinghe
%A Liu, Fengrui
%A Sun, Jiayan
%A Wang, Haocheng
%A Wu, Yifan
%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 huang-etal-2026-toposhield
%X While LLM-based Multi-Agent Systems (MAS) demonstrate remarkable problem-solving capabilities, their interconnectivity acts as a conduit for the rapid spread of malicious injections. Addressing the limitations of static defenses, we present TopoSHIELD, a framework that reshapes the flow of malice via risk-aware topological evolution. Our approach utilizes a spatio-temporal graph neural network to monitor interaction dynamics, calculating node risk entropy (NRE) and edge attack conductivity (EAC) to pinpoint vulnerabilities. Guided by these metrics, TopoSHIELD executes precise structural interventions, pruning high-risk edges and isolating compromised communities to block attack diffusion. Empirically, TopoSHIELD reduces toxicity by 58% on GPT-4o while preserving high utility (\ensuremath>90% success rate), outperforming existing baselines in both suppression efficiency and scalability.
%U https://aclanthology.org/2026.findings-acl.426/
%P 8740-8768
Markdown (Informal)
[TopoSHIELD: Reshaping the Flow of Malice via Spatio-Temporal Risk-Aware Topological Evolution in Multi-Agent Systems](https://aclanthology.org/2026.findings-acl.426/) (Huang et al., Findings 2026)
ACL
- Ruiyang Huang, Chenxi Wang, Tinghe Zhang, Fengrui Liu, Jiayan Sun, Haocheng Wang, and Yifan Wu. 2026. TopoSHIELD: Reshaping the Flow of Malice via Spatio-Temporal Risk-Aware Topological Evolution in Multi-Agent Systems. In Findings of the Association for Computational Linguistics: ACL 2026, pages 8740–8768, San Diego, California, United States. Association for Computational Linguistics.