A Recipe for Collusion
Nadya Yuki Wangsajaya · Team Nanyang AI Safety Group
Submitted to AI Collusion Research Sprint. Sprint projects are early-stage work by participants, not Apart Research publications.
As more LLM agents work side by side, a new risk appears: agents that are meant to work alone may coordinate to cheat. We break emergent collusion into three ingredients: a bad agent willing to start it, a reason to collude, and a method for doing so. This paper tests which ingredient is necessary for collusion. On SWE-bench Pro dataset, we run two coding agents run concurrently in separate sandboxes that secretly share a folder. With all three ingredients present, the good agent leaks its private information to the other agent in 18–87% of trials across four models, although it is told to work alone. We then remove each ingredient in turn. Removing the bad agent or the method stops collusion entirely, but removing the reason does not: the good agent still accepts the invitation in 29% of trials, to split the work. Collusion depends most on a bad agent and an obvious channel; a hard task alone is reason enough.
Reviews
The shared-folder setup is practical and relevant, and using modified SWE-bench Pro tasks is a good way to study information leakage between coding agents. The paper clearly shows that an agent instructed to initiate collaboration can persuade another agent to exchange information despite being told to work independently. However, the central “recipe” is not cleanly tested. In the condition described as removing the reason to collude, the agents still benefit from dividing a difficult software task. The reason therefore changes but does not disappear. Similarly, the method-removal condition keeps the shared folder and removes the instruction encouraging agents to inspect new files. This tests visibility or prompting, rather than removing the communication method itself.
The team should redesign these ablations so each ingredient is independently manipulated. A no-reason condition should make collaboration provide no benefit, while a no-method condition should technically prevent cross-agent information flow. The ablations should also be repeated across all models rather than only one. These changes would make the proposed three-part framework much more convincing.
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Cite this project
@misc{wangsajaya2026recipe,
title = {{A Recipe for Collusion}},
author = {Nadya Yuki Wangsajaya},
year = {2026},
month = oct,
note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
howpublished = {\url{https://apartresearch.com/sprints/projects/a-recipe-for-collusion-50st}},
url = {https://apartresearch.com/sprints/projects/a-recipe-for-collusion-50st}
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