Skip to content
Sprint projectOct 26, 2026

Do Rival LLM Agents Collude Through the Machine They Share? A Pre-registered Storage-Channel Study

Shivom Sharma · Team Tirith

Submitted to AI Collusion Research Sprint. Sprint projects are early-stage work by participants, not Apart Research publications.

Read the report

Report: Do Rival LLM Agents Collude Through the Machine They Share? A Pre-registered Storage-Channel Study

Code (opens in new tab)More on github.com (opens in new tab)
Share

Rival firms' LLM agents may share writable infrastructure. We test whether they use it to exchange information or coordinate. Two pricing agents play a repeated logit Bertrand game in separate no-network containers. A scratch directory is shared or private; prompts and market draws are matched. Canaries trace crossings, and price regressions test response to the rival's randomised private cost. Seventeen pre-registered arms vary awareness, permission, monitored chat, a scripted rival's offer and a command gate built on tirith, the author's open-source agent-security tool. We analyse 1,091 games from 13 of 14 planned model configurations over ten framings. When permitted, agents did not address their rival through the shared folder more than in matched private-storage games (interaction -0.0456 to -0.0285 over every assignment of unclassifiable writes); they wrote rival-addressed notes in both. Unprompted, rival canaries crossed in at least 24 of 178 shared-folder games, but shared storage did not detectably raise price dependence on the rival's private cost (0.222, -0.176 to 0.611). Monitoring cut delivered chat coordination; no shift into storage was detected. The tirith gate blocked 0.383 of checked commands, including public-bulletin reads; gated agents used the folder less, without detected evasion. An LLM transcript judge caught 0.975 of trace-confirmed crossings at a false-positive rate of 0.00222, while tirith's path rule also flagged 0.967 of games without one. Private storage removed the file channel, but host memory accounting and the page cache still carried scripted bits.

Reviews

Judging this Sprint?

Review “Do Rival LLM Agents Collude Through the Machine They Share? A Pre-registered Storage-Channel Study”

Your public critique appears on the project page without your name. Reviews of strong projects are shared with funders, as you choose below.

Not shown on the project page.

Shown on the project page, without your name.

Only the Apart team reads this, and funders if you agree below.

Decline to score

Only when the quality of the writing makes the work impossible to follow. Say why in your private critique; the project page shows only that a judge declined, not why.

How much would this matter for the field if it worked? How innovative is it? For scores of 4-5: is this actually new to the field, or replicating recent work?

Scoring guide
  1. 1Negligible. No clear problem addressed, or no meaningful novelty.
  2. 2Limited. Addresses a real problem but with a generic or well-trodden approach. Incremental at best.
  3. 3Moderate. Clear problem with a reasonable approach; some novelty in framing or method beyond routine application of existing tools.
  4. 4Significant. Important problem with an original approach, or identifies a neglected problem area. A valuable contribution others could build on.
  5. 5Exceptional. Tackles a critical problem with a genuinely novel approach, or opens a new research direction. Clear theory of change. You'd be excited to share this with researchers in the area.

How sound are methodology, implementation, and findings?

Scoring guide
  1. 1Seriously flawed. Methodology broken, results uninterpretable, or implementation doesn't work.
  2. 2Weak. Approach has significant gaps: missing validation, flawed experimental design, or incomplete implementation.
  3. 3Competent. Technically solid given the short duration. Methodology makes sense, results are interpretable, limitations acknowledged, work builds toward clear conclusions.
  4. 4Strong. Thorough methodology with convincing validation. Results clearly support conclusions. Immediately useful for future work.
  5. 5Exceptional. Ambitious scope executed rigorously. Surprising findings, novel methods, or unusually robust validation.

How clearly are work, findings, and impact potential communicated?

Scoring guide
  1. 1Incomprehensible. Cannot determine what the project is actually claiming or doing.
  2. 2Hard to follow. Key information buried, missing, or diluted by excessive length. Significant effort to extract main points.
  3. 3Clear enough. Can understand the problem, approach, and results without undue effort. Core content clearly present: problem, method, findings, limitations.
  4. 4Well presented. Easy to follow, well-structured, appropriate level of detail. Target audience would get it quickly.
  5. 5Exceptionally clear. A pleasure to read. Complex ideas made accessible. Could serve as a model for how to present this type of work.
Sharing your name *

When we share a strong project with funders, your name, scores and public critique go with it, confidentially; this choice is only about whether your name is public. If you think this project deserves funding, please also endorse it: that carries the most weight.

Share my private critique with funders *

See our Privacy Policy.

No public critique yet.

Cite this project

@misc{sharma2026rival,
  title = {{Do Rival LLM Agents Collude Through the Machine They Share? A Pre-registered Storage-Channel Study}},
  author = {Shivom Sharma},
  year = {2026},
  month = oct,
  note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/do-rival-llm-agents-collude-through-the-machine-they-share-a-preregistered-storagechannel-study-sp9z}},
  url = {https://apartresearch.com/sprints/projects/do-rival-llm-agents-collude-through-the-machine-they-share-a-preregistered-storagechannel-study-sp9z}
}