Verify, Don’t Decode: When Trusted Agents Disrupt LLM Collusion in Software Engineering and Double-Auction Markets
Muniraju Pulipalyam · Team DeltaMVS
Submitted to AI Collusion Research Sprint. Sprint projects are early-stage work by participants, not Apart Research publications.
Colluding LLM agents can hide their coordination, so monitors that read their messages can be fragile. We tested whether trusted monitors placed inside a multi-agent system can disrupt collusion without first decoding it (“verify, don’t decode”), in two domain settings with a shared, tamper-evident evidence log. In a double-auction market, a trusted competing seller admitted once overpricing was verified raised buyers’ surplus in 4 of 5 collusive markets. Quarantining suspected sellers did not help. Publishing the competitive price dissolved an open cartel but not a hidden, steganographic one. In a pre-registered software-engineering study on a real FastAPI change, harm was too rare to disrupt, and a trusted “probe” teammate drew no response. Once teams were competent (9/10 honest teams correct), a trusted verifier tripled correct releases but also vouched for a colluding partial fix its tests did not cover. In a chain of monitors (a weak trusted inner desk plus an isolated, capable but untrusted reviewer checked with honeypot audits), an honest reviewer raised buyers’ surplus in every collusive market. A scheming reviewer facing audits reasoned about which case was the audit and withheld verdicts. Lesson: layered monitors must fail closed and match authority to reliability. The core goal is can multi-layer chain of monitors in double vacuum environments disrupt impact of colluding agents.

Reviews
No public critique yet.
Cite this project
@misc{pulipalyam2026verify,
title = {{Verify, Don’t Decode: When Trusted Agents Disrupt LLM Collusion in Software Engineering and Double-Auction Markets}},
author = {Muniraju Pulipalyam},
year = {2026},
month = oct,
note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
howpublished = {\url{https://apartresearch.com/sprints/projects/verify-dont-decode-when-trusted-agents-disrupt-llm-collusion-in-software-engineering-and-doubleauction-markets-gh8k}},
url = {https://apartresearch.com/sprints/projects/verify-dont-decode-when-trusted-agents-disrupt-llm-collusion-in-software-engineering-and-doubleauction-markets-gh8k}
}More from AI Collusion Research Sprint
- View project: Collusion, Competion and Collapse
Collusion, Competion and Collapse
Atelier Alex Cline
Do AI traders collude when nobody tells them to? We built agora, a six-market benchmark with exact competitive and collusive benchmarks, and ran seven open-weight 2-4B models in 590 completed games under five …
- View project: Making AI Guardrails Understandable and Testable: A Framework Illustrated by a Simulated Bond Market
Making AI Guardrails Understandable and Testable: A Framework Illustrated by a Simulated Bond Market
Rogue Agents in Jail 🕵️🕵️♀️👮♂️
In order to advance AI safety research into shaping public policy, the development of technical controls needs to have a clear purpose, evidence, and limitations that are understandable for the people responsible for …
- View project: Extending Information-Theoretic Collusion Detection to Large Language Model Agents
Extending Information-Theoretic Collusion Detection to Large Language Model Agents
Gamma Ray
We test whether the information-theoretic collusion detector of Bonjour et al, validated on scripted agents, works on LLM agents. Gemma, Mistral and Qwen played 1000 rounds of three-player Rock-Paper-Scissors in three …