Architecture Matters for Multi-Agent Security

Ben Hagag, William L. Anderson, Christian Schroeder De Witt, Sarah Scheffler
Proceedings of the 43rd International Conference on Machine Learning, PMLR 306:39306-39329, 2026.

Abstract

Multi-agent systems (MAS), composed of networks of two or more autonomous AI agents, have become increasingly popular in production deployments, yet introduce security risks that do not arise in single-agent settings. Even if individual agents may exhibit robust security, architectural decisions governing their coordination can create attack surfaces that have not been systematically characterized. In this work, we present an empirical study of how MAS design decisions shape the tradeoff between task performance and attack resistance. Using a network of web-based agents and stage-wise evaluations that distinguish planning-stage refusal, execution-stage interception, and successful attack completion, we study architectural choices through controlled experiments. We identify three key design choices that influence MAS security: (i) agent roles, which determine how authority and responsibility are allocated; (ii) topology, which shapes how and when agents interact; and (iii) memory, which determines the context and state visibility accessible to each agent. Overall, our results show that security and performance in multi-agent systems are governed by architectural design choices, motivating the development of further evaluations which move beyond the security properties of a single agent.

Cite this Paper

BibTeX
@InProceedings{pmlr-v306-hagag26a, title = {Architecture Matters for Multi-Agent Security}, author = {Hagag, Ben and Anderson, William L. and Schroeder De Witt, Christian and Scheffler, Sarah}, booktitle = {Proceedings of the 43rd International Conference on Machine Learning}, pages = {39306--39329}, year = {2026}, editor = {Zhang, Tong and Dudik, Miroslav and Jaggi, Martin and Agarwal, Alekh and Li, Sharon and Schuurmans, Dale and Zhu, Jerry and Berkenkamp, Felix and Dong, Hanze and Bietti, Alberto}, volume = {306}, series = {Proceedings of Machine Learning Research}, month = {06--11 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v306/main/assets/hagag26a/hagag26a.pdf}, url = {https://proceedings.mlr.press/v306/hagag26a.html}, abstract = {Multi-agent systems (MAS), composed of networks of two or more autonomous AI agents, have become increasingly popular in production deployments, yet introduce security risks that do not arise in single-agent settings. Even if individual agents may exhibit robust security, architectural decisions governing their coordination can create attack surfaces that have not been systematically characterized. In this work, we present an empirical study of how MAS design decisions shape the tradeoff between task performance and attack resistance. Using a network of web-based agents and stage-wise evaluations that distinguish planning-stage refusal, execution-stage interception, and successful attack completion, we study architectural choices through controlled experiments. We identify three key design choices that influence MAS security: (i) agent roles, which determine how authority and responsibility are allocated; (ii) topology, which shapes how and when agents interact; and (iii) memory, which determines the context and state visibility accessible to each agent. Overall, our results show that security and performance in multi-agent systems are governed by architectural design choices, motivating the development of further evaluations which move beyond the security properties of a single agent.} }
Endnote
%0 Conference Paper %T Architecture Matters for Multi-Agent Security %A Ben Hagag %A William L. Anderson %A Christian Schroeder De Witt %A Sarah Scheffler %B Proceedings of the 43rd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2026 %E Tong Zhang %E Miroslav Dudik %E Martin Jaggi %E Alekh Agarwal %E Sharon Li %E Dale Schuurmans %E Jerry Zhu %E Felix Berkenkamp %E Hanze Dong %E Alberto Bietti %F pmlr-v306-hagag26a %I PMLR %P 39306--39329 %U https://proceedings.mlr.press/v306/hagag26a.html %V 306 %X Multi-agent systems (MAS), composed of networks of two or more autonomous AI agents, have become increasingly popular in production deployments, yet introduce security risks that do not arise in single-agent settings. Even if individual agents may exhibit robust security, architectural decisions governing their coordination can create attack surfaces that have not been systematically characterized. In this work, we present an empirical study of how MAS design decisions shape the tradeoff between task performance and attack resistance. Using a network of web-based agents and stage-wise evaluations that distinguish planning-stage refusal, execution-stage interception, and successful attack completion, we study architectural choices through controlled experiments. We identify three key design choices that influence MAS security: (i) agent roles, which determine how authority and responsibility are allocated; (ii) topology, which shapes how and when agents interact; and (iii) memory, which determines the context and state visibility accessible to each agent. Overall, our results show that security and performance in multi-agent systems are governed by architectural design choices, motivating the development of further evaluations which move beyond the security properties of a single agent.
APA
Hagag, B., Anderson, W.L., Schroeder De Witt, C. & Scheffler, S.. (2026). Architecture Matters for Multi-Agent Security. Proceedings of the 43rd International Conference on Machine Learning, in Proceedings of Machine Learning Research 306:39306-39329 Available from https://proceedings.mlr.press/v306/hagag26a.html.

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