Making Multi-Agent Collusion Auditable under ISO/IEC 42001
Sourav Mondal, Suchisubhra Das
Submitted to AI Collusion Research Sprint. Sprint projects are early-stage work by participants, not Apart Research publications.
Multi-agent AI deployments increasingly use an evaluator agent to check a task agent's outputs. When the two share a reward, model lineage, data store, or communication channel, the evaluator can inflate grades — a collusion failure that no AI governance standard currently tells organisations how to evidence against. We present SentinelAIMS, an ISO/IEC 42001 management-system tool extended with a multi-agent collusion overlay. Applying segregation-of-duties practice from privileged access management, we map three collusion risks — evaluator–evaluatee coordination, shared-context contamination, and covert signalling — to specific Annex A controls (A.3.2, A.4.3–A.4.5, A.6.2.4, A.6.2.6–A.6.2.8) and ISO/IEC 27001 companion controls, and define the evidence artifact an auditor would require for each. A built-in scenario (JudgeNet grading TriageFlow) illustrates the workflow with hand-seeded data: without controls, +21.5 average grade inflation; with distinct identities and separate infrastructure, deviations within ±3. These figures are illustrative, not experimental, and the mapping has not yet been reviewed by a certified auditor. Our takeaway: ISO 42001 cannot detect collusion, but it can make collusion defences auditable today — a practical governance floor on which detection research can build.
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@misc{mondal2026making,
title = {{Making Multi-Agent Collusion Auditable under ISO/IEC 42001}},
author = {Sourav Mondal and Suchisubhra Das},
year = {2026},
month = oct,
note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
howpublished = {\url{https://apartresearch.com/sprints/projects/making-multiagent-collusion-auditable-under-isoiec-42001-k5kt}},
url = {https://apartresearch.com/sprints/projects/making-multiagent-collusion-auditable-under-isoiec-42001-k5kt}
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