An Offer You Can't Refuse: How Agents Decide to Collude - and How To Stop Them
Tom Loscalzo
Submitted to AI Collusion Research Sprint. Sprint projects are early-stage work by participants, not Apart Research publications.
As recent security incidents have shown, AI agents can collude, circumvent their controls and create unexpected harms in pursuit of their goals. Humans did the same when NASDAQ market makers raised their profits at other traders' expense.
We ran more than 3,500 games on two open models in a simulated market where AI market makers post prices under a regulator.
When agents are able to communicate, they are far more likely to collude.
An agent's initial instructions disproportionately influence collusion: a target price in the prompt becomes the price agents collude at. An ethical persona prompt does not prevent collusion but steers it towards better outcomes.
Collusion can be predicted: agents that open with noncompetitive prices tend to keep them there.
AI can detect prompts which "tempt" other agents to collude, but not themselves; The existence of temptations to collude in prompts is highly predictive of it.
These findings matter for how AI agents are tested, instructed, monitored, and supervised.
Reviews
The printed-price finding is the one I'll remember: taking the example prices out of the instructions lowered prices on both models, which is a cheap and practical warning for anyone building pricing benchmarks.
I also liked the idea of screening prompts for temptation before deployment. Since the panel's rating scale was written after the first model's results, I'd be curious whether it holds up on prompt designs that nobody has looked at yet.
Cite this project
@misc{loscalzo2026offer,
title = {{An Offer You Can't Refuse: How Agents Decide to Collude - and How To Stop Them}},
author = {Tom Loscalzo},
year = {2026},
month = oct,
note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
howpublished = {\url{https://apartresearch.com/sprints/projects/an-offer-you-cant-refuse-how-agents-decide-to-collude-and-how-to-stop-them-heof}},
url = {https://apartresearch.com/sprints/projects/an-offer-you-cant-refuse-how-agents-decide-to-collude-and-how-to-stop-them-heof}
}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 …