Skip to content
Sprint projectOct 26, 2026Hong Kong (Online)

LatticeGuard: Post-Quantum Audit Trails for Detecting and Reproducing Collusion in Multi-Agent AI Systems

Martin Yeung · Team LatticeGuard

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

Read the report

Report: LatticeGuard: Post-Quantum Audit Trails for Detecting and Reproducing Collusion in Multi-Agent AI Systems

Share

Multi-agent AI systems increasingly use natural-language messages, tools, and shared workspaces, creating opportunities for agents to coordinate in ways that evade transcript-level oversight. Existing collusion audits can detect suspicious behavior, but they often depend on mutable logs, trusted central auditors, or white-box access to model activations. This project proposes LatticeGuard, a post-quantum audit layer that makes collusion investigations reproducible, tamper-evident, and privacy-preserving. The core idea is to combine collusion detectors with a lattice-based cryptographic evidence protocol: each agent event is committed into an append-only audit ledger using post-quantum signatures, Merkle commitments, and optional lattice-based zero-knowledge proofs for selective disclosure. Auditors can later verify that transcripts, tool calls, detector scores, and interventions were not modified, while sensitive prompts or user data remain hidden unless policy conditions require disclosure. We apply this protocol to three collusion families—steganographic coordination, hidden coalition behavior, and monitor-policy collusion—and define evaluation metrics for detection utility, audit reproducibility, tamper resistance, privacy leakage, and overhead. LatticeGuard is valuable because it addresses a gap between “detecting collusion” and “proving what happened” in a quantum-resilient way. The main takeaway is that future AI safety audits should treat cryptographic provenance as a first-class component of collusion detection, not as an afterthought.

Reviews

Judging this Sprint?

Review “LatticeGuard: Post-Quantum Audit Trails for Detecting and Reproducing Collusion in Multi-Agent AI Systems”

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.

  1. The paper argues that collusion audits need more than detectors. They also need evidence that is tamper-evident, reproducible and privacy-preserving: proof of what a monitor saw, which version of the detector ran, and that logs weren’t altered afterwards. That reframing is reasonable, and the paper is clearly organized. The event and verdict schemas and the four-configuration comparison are sensible starting points. But the submission is a proposal, not a research result, and several design choices need more justification.

    No implementation or results. “Code and Data: Not yet available.” Section 4 describes a prototype that should be built and findings that are expected. The appendix lays out a 12-hour plan (Merkle log, signature abstraction, one detector, tampered runs, replay verifier) that would fit in a hackathon. Even a minimal version would have turned this into an evaluable contribution. As it stands, none of the claims can be checked.

    The novelty claim is overstated. Append-only Merkle logs with signed checkpoints and independent witnesses are an established way to make logs tamper-evident, from secure-logging work in the late 1990s to Certificate Transparency (RFC 6962), Trillian and Sigstore/Rekor. Applying them to AI agent events is a sensible engineering step, but it isn’t a “new research direction.” Related work should cover this literature, along with C2PA-style provenance, TEE attestation and zkML, and explain what is specific to collusion auditing.

    The post-quantum layer is mostly branding. The part of the design that actually ensures tamper evidence is the hash-based Merkle log with witnesses, and hash functions are already only modestly affected by quantum attacks. For signatures, the quantum threat is forging signatures in the future. Timestamped checkpoints, hash-based anchors or periodic re-signing already handle that for archived evidence. Swapping ECDSA for ML-DSA is close to a library change. The paper should explain why post-quantum signatures change the security of collusion audits in a meaningful way, or present them as an optional hardening step.

    The threat model is unclear, and the main trust problem goes unaddressed. Colluding agents usually can’t edit host logs. The party who can is the operator, and the operator holds the signing keys and controls what gets instrumented. A Merkle log only shows that records weren’t changed after they were committed. It can’t show that the records were complete or truthful when they were written, or that agents didn’t act through channels outside the instrumentation. That is acknowledged in the limitations, but it is the central issue for “proving what happened” and needs to be part of the design itself, e.g. via TEE attestation of the capture runtime or a capture layer independent of the operator.

    Selective disclosure conflicts with reproducibility. For an auditor to reproduce a detector score, they need all of the detector’s inputs. Revealing only a subset works only if there is a zero-knowledge proof that the score was computed correctly. For LLM judges or activation probes, that means zkML, which is currently impractical at that scale. Lattice-based ZK is also an unusual choice compared with hash-based STARKs, which are already plausibly post-quantum. This is the hardest part of the proposal and the most hand-waved.

    Several proposed metrics would pass automatically. A correctly implemented Merkle log detects any deletion, insertion or reordering after a checkpoint, so “≥99% tamper detection” can’t fail. “No reduction in detection quality” from logging is likewise expected by construction. More useful measurements would be overhead under realistic agent workloads, and what fraction of collusion-relevant activity (side channels, timing, external tools) the instrumentation actually captures.

    Suggestions: Build the appendix prototype around an existing benchmark (e.g. Colosseum or a steganography task). Show a full end-to-end example: an alert, an attempted tamper that gets detected, and a successful replay. Report the overhead. Narrow the claims to what tamper-evident logging adds for collusion audits specifically.

    Read full reviewShow less

Cite this project

@misc{yeung2026latticeguard,
  title = {{LatticeGuard: Post-Quantum Audit Trails for Detecting and Reproducing Collusion in Multi-Agent AI Systems}},
  author = {Martin Yeung},
  year = {2026},
  month = oct,
  note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/latticeguard-postquantum-audit-trails-for-detecting-and-reproducing-collusion-in-multiagent-ai-systems-ifmn}},
  url = {https://apartresearch.com/sprints/projects/latticeguard-postquantum-audit-trails-for-detecting-and-reproducing-collusion-in-multiagent-ai-systems-ifmn}
}