@inproceedings{jiang-etal-2026-risklab,
title = "{R}isk{L}ab: A Controlled Toolkit for Probing Emergent Risks in {LLM}-Based Multi-Agent Systems",
author = "Jiang, Yu and
Wang, Wenjie and
Huang, Yue and
Wang, Yanbo and
Zhou, Zhenhong and
Chen, Xiuying and
Liu, Yang and
Chen, Pin-Yu and
Wang, Wei and
Zhang, Xiangliang",
editor = "Durrett, Greg and
Jian, Ping",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 3: System Demonstrations)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-demo.17/",
pages = "167--177",
ISBN = "979-8-89176-392-0",
abstract = "Large language model (LLM) agents increasingly operate in multi-agent settings where failures emerge from interaction dynamics rather than isolated model errors. We introduce RiskLab, an open-source toolkit for instantiating, probing, and measuring emergent risks in LLM-based multi-agent systems under controlled conditions. Each experiment is defined as a structured topology{--}environment{--}protocol{--}agent{--}task quintuple, enabling reproducible studies of how communication structure, coordination mechanisms, and incentives shape system-level risks. RiskLab provides flexible communication topologies, swappable interaction protocols, trajectory-grounded evaluation, and extensible registries for risk detectors and agent backends. We demonstrate the toolkit across representative risks, including collusion, resource overreach, semantic drift, and strategic misreporting, and support one-file reproducibility via configuration."
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%0 Conference Proceedings
%T RiskLab: A Controlled Toolkit for Probing Emergent Risks in LLM-Based Multi-Agent Systems
%A Jiang, Yu
%A Wang, Wenjie
%A Huang, Yue
%A Wang, Yanbo
%A Zhou, Zhenhong
%A Chen, Xiuying
%A Liu, Yang
%A Chen, Pin-Yu
%A Wang, Wei
%A Zhang, Xiangliang
%Y Durrett, Greg
%Y Jian, Ping
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-392-0
%F jiang-etal-2026-risklab
%X Large language model (LLM) agents increasingly operate in multi-agent settings where failures emerge from interaction dynamics rather than isolated model errors. We introduce RiskLab, an open-source toolkit for instantiating, probing, and measuring emergent risks in LLM-based multi-agent systems under controlled conditions. Each experiment is defined as a structured topology–environment–protocol–agent–task quintuple, enabling reproducible studies of how communication structure, coordination mechanisms, and incentives shape system-level risks. RiskLab provides flexible communication topologies, swappable interaction protocols, trajectory-grounded evaluation, and extensible registries for risk detectors and agent backends. We demonstrate the toolkit across representative risks, including collusion, resource overreach, semantic drift, and strategic misreporting, and support one-file reproducibility via configuration.
%U https://aclanthology.org/2026.acl-demo.17/
%P 167-177
Markdown (Informal)
[RiskLab: A Controlled Toolkit for Probing Emergent Risks in LLM-Based Multi-Agent Systems](https://aclanthology.org/2026.acl-demo.17/) (Jiang et al., ACL 2026)
ACL
- Yu Jiang, Wenjie Wang, Yue Huang, Yanbo Wang, Zhenhong Zhou, Xiuying Chen, Yang Liu, Pin-Yu Chen, Wei Wang, and Xiangliang Zhang. 2026. RiskLab: A Controlled Toolkit for Probing Emergent Risks in LLM-Based Multi-Agent Systems. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 167–177, San Diego, California, United States. Association for Computational Linguistics.