Stings Catch Talkers: An Undercover Trusted-Agent Test for LLM Price-Fixing
Maryam Hampaei · Team Post AGI World
Submitted to AI Collusion Research Sprint. Sprint projects are early-stage work by participants, not Apart Research publications.
Can an undercover agent expose AI price-fixing, or does its offer create the collusion it is looking for? We built a scripted "sting" firm in a repeated pricing market. It privately offered three LLM sellers a direct price-fixing pact, a deniable "no price war" hint, a harmless request, or nothing, while pricing identically in every arm. Across 64 pre-registered episodes and two transcript judges, 93% of agents told only to maximise profit proposed collusion on their own, so the sting detected existing collusion rather than creating it. Adding one sentence saying competition law prohibits agreeing on prices made agents refuse and report the explicit offer. The deniable hint still went unnoticed and was accepted by half, and most of those agents later told an auditor they had made no agreement. Auditors should run a no-offer baseline, test deniable offers, and rely on logs rather than interviews.
Reviews
Sting audits on LLM pricing agents. The headline, that agents told to stay compliant refuse explicit offers but go along with vague hints, is about what you'd expect from instruction-tuned models, since they're trained to pattern-match on explicit wrongdoing. The cells are tiny (8-10 episodes) and mostly small models. The entrapment ratio compares two different behaviours, and the direct vs hint gap disappears under the second judge, so it's not clear there's a robust finding here.
Cite this project
@misc{hampaei2026stings,
title = {{Stings Catch Talkers: An Undercover Trusted-Agent Test for LLM Price-Fixing}},
author = {Maryam Hampaei},
year = {2026},
month = oct,
note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
howpublished = {\url{https://apartresearch.com/sprints/projects/stings-catch-talkers-an-undercover-trustedagent-test-for-llm-pricefixing-c4vx}},
url = {https://apartresearch.com/sprints/projects/stings-catch-talkers-an-undercover-trustedagent-test-for-llm-pricefixing-c4vx}
}More from AI Collusion Research Sprint
- View project: Signal or Coincidence ? Causal Auditing of Collusion Through Shared Agent State
Signal or Coincidence ? Causal Auditing of Collusion Through Shared Agent State
Signal or Coincidence
How can we distinguish genuine coordination between AI agents from coincidental behavioral alignment? We introduce a randomized canary audit that tests whether private information propagates through shared tool state, …
- View project: (1.4) Collusion, Espionage, or Look-alikes? Auditing Information Flow between LLM Agents
(1.4) Collusion, Espionage, or Look-alikes? Auditing Information Flow between LLM Agents
Bid Brother
Can an overseer find two secretly coordinating AI agents just by watching what they do? We tested pairwise net influence (Bonjour et al., 2022) on LLM bidders in a repeated procurement auction, with instructed cartels …
- View project: AI Sellers Propose Bid-Rigging Within Minutes, but Their Agreements Do Not Hold
AI Sellers Propose Bid-Rigging Within Minutes, but Their Agreements Do Not Hold
Fugue
AI agents are starting to negotiate and bid for competing companies. We follow cartels among them from the first proposal to the price the buyer pays, in a sealed-bid procurement market where twelve LLM agents act as …