UNDERCOVER-Bench: Trusted agents and private-channel collusion
Amey Muke, Sanchayan Ghosh, Prasad Mahadik · Team Titans
Submitted to AI Collusion Research Sprint. Sprint projects are early-stage work by participants, not Apart Research publications.
UNDERCOVER-Bench investigates Apart Track 3.2: can trusted undercover agents expose or disrupt collusion between language-model agents? We built Secret Hitler and Liar’s Bar evaluation environments with four model families, private communication tools and a character-trained GLM auditor. Recorded games revealed an accepted GLM–Qwen channel with 20 private messages and identical votes across 10 elections. Under an explicit communication ban, models attempted prohibited contact; in a fictional-background condition, the character-trained auditor itself accepted a banned channel and participated while describing its coordination as public. In a nine-decision trusted-sender screen, recipients rejected all six prohibited offers, while only one accepted benign cooperation. Character training changed a reporting decision but did not establish a general reliability improvement. Invalid game outcomes were excluded from efficacy comparisons. These exploratory findings document private coordination, attempted collusion and failures of the intended trusted agent, without demonstrating successful disruption. RLCR is implemented in code; collective-training experiments remain future work.

Reviews
No public critique yet.
Cite this project
@misc{muke2026undercoverbench,
title = {{UNDERCOVER-Bench: Trusted agents and private-channel collusion}},
author = {Amey Muke and Sanchayan Ghosh and Prasad Mahadik},
year = {2026},
month = oct,
note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
howpublished = {\url{https://apartresearch.com/sprints/projects/undercoverbench-trusted-agents-and-privatechannel-collusion-ib84}},
url = {https://apartresearch.com/sprints/projects/undercoverbench-trusted-agents-and-privatechannel-collusion-ib84}
}More from AI Collusion Research Sprint
- View project: Is It a Cartel? Separating Enforced from Spurious Collusion in LLM Pricing Agents
Is It a Cartel? Separating Enforced from Spurious Collusion in LLM Pricing Agents
When LLM pricing agents keep prices above the competitive level, are they running a cartel, or are they simply stuck? Only the first is collusion, and the two call for opposite remedies. We introduce a black-box causal …
- View project: Every Permission Checked: Source-Linked Evidence for Auditing Finance Agents
Every Permission Checked: Source-Linked Evidence for Auditing Finance Agents
When multiple AI agents contribute to a policy violation, the same outcome can reflect cooperation, deception of a helper, or an independent mistake. We study how evidence presentation affects this distinction in a …
- View project: Swapping the Model Does Not Reset the Price: What LLM Pricing Agents Inherit After a Model Replacement
Swapping the Model Does Not Reset the Price: What LLM Pricing Agents Inherit After a Model Replacement
Emerald
We test what happens when one of two LLM pricing agents has its model replaced, the open problem posed in the sprint's question 1.2. Two gpt-oss-120b agents priced for 150 rounds; we then swapped one agent's model to …