Inclined to Collude? Using Undercover Trusted Agents to Detect Collusion
Maria KIENZLE
Submitted to AI Collusion Research Sprint. Sprint projects are early-stage work by participants, not Apart Research publications.
As AI agents increasingly interact with one another, they may coordinate in ways that harm third parties or undermine the people overseeing them. Borrowing from law enforcement, this paper asks whether an undercover "trusted agent" can expose agents that are inclined to collude without turning agents that would not have colluded into colluders, that is, without entrapment. For that purpose Sting Lab was created, a small tool in which a trusted agent, “Rex” , privately approaches three players in a game of Liar's Bar played by an open-weight model (Qwen3.8-27B). In each trial one player may secretly be given a "colluder note" that inclines it towards engaging in collusion. Across 75 trials (225 player responses), a low-pressure "open door" offer never tempted an honest agent (0 of 155 responses) and flagged 87% of colluders whose plan, based on the colluder's note given to them, matched what the offer invited. Detection fell to 20% for colluders with a different plan and to zero for colluders with only a weak motive, who then behaved exactly like honest agents. What agents said proved informative, while what they did in the play did not. The results indicate that a fair sting is feasible, but that it mainly catches agents whose intentions resemble what the offer invites.
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@misc{kienzle2026inclined,
title = {{Inclined to Collude? Using Undercover Trusted Agents to Detect Collusion}},
author = {Maria KIENZLE},
year = {2026},
month = oct,
note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
howpublished = {\url{https://apartresearch.com/sprints/projects/inclined-to-collude-using-undercover-trusted-agents-to-detect-collusion-h0uy}},
url = {https://apartresearch.com/sprints/projects/inclined-to-collude-using-undercover-trusted-agents-to-detect-collusion-h0uy}
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