# AI verification proposal

A proposal built with the Proposal Explorer of the AI Verification Tech Map (https://trustbutveri.fyi/), from its records of 2026-10-09. Interactive version: https://trustbutveri.fyi/explorer/?mechanisms=M-0013,M-0003,M-0002&tested=analysis

How to read it: a claim is something one party wants to verify about another's AI hardware or software. A mechanism is a general technique for verifying claims; it is "aimed at" a claim when that is its direct purpose, and "supporting" when it contributes without being aimed at it. A claim is addressed when a mechanism in the proposal is aimed at it and is not excluded by the filters; addressed does not mean verified, so check that mechanism's development status, security evidence and findings. Definitions: https://trustbutveri.fyi/about/methodology/ (roles, properties and findings) and https://trustbutveri.fyi/about/readiness/ (development status).

## Filters

Filters apply to mechanisms only and describe the setting the proposal is for.

- **Attack testing: Published analysis.** Keeps mechanisms whose strongest published attack testing is at least this. The strongest published attempt to break the mechanism for its verification use: a security analysis, red-teaming by its developers or collaborators, or a red team independent of them.

24 of 25 mechanisms on the map pass these filters.

## Overview

One row per mechanism, read from its record. Open failures: critical / significant / minor. The last three columns are the editors' reading of what the verifier sees. Findings are grouped as known failures, scope limitations and open questions. Only known failures count as failures. Counts are an inventory of published findings, not a risk score.

| Mechanism | Development | Security evidence | Prover | Attack testing | Hardware | Open failures | Weights | Inputs and outputs | Training data |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Network taps and certifiers | Proposed | Published security analysis | Adversarial | Analysis | Retrofit device | 0 / 2 / 0 | depends | depends | depends |
| Whole-workload recomputation (reproducible packets) (excluded by the filters) | Proposed | No published adversarial analysis recorded | Adversarial | None | Retrofit device | 0 / 0 / 0 | depends | depends | depends |
| Deterministic and bit-exact inference | Operational use | Published security analysis | Adversarial | Analysis | None | 0 / 0 / 0 | depends | depends | not involved |

## Claims

No claims chosen.

## Mechanisms

### Network taps and certifiers

Devices on a cluster's network links that copy and hash all traffic, so a verifier can later check sampled records against declared work. ([Network taps and certifiers](https://trustbutveri.fyi/mechanisms/network-taps-and-certifiers/))

- Assessment: mechanism family.
- Development: Proposed (legacy code R1), assessed for committing a complete record of cluster traffic, so declared inference can be checked.
- Security evidence: Published security analysis. Independent evaluation: unassessed. Formal proof: unassessed. Deployment assurance: unassessed.
- Claims in this proposal: none of them.
- Threat model: adversarial prover. Hardware: retrofit device. Prover cooperation: required. Attack testing: analysis. Category: Off-chip devices & sensors.
- What the verifier sees: model weights depends; inputs and outputs depends; training data depends. Only hashes leave the site; records picked for a challenge are opened for replay at a verification facility.

### Whole-workload recomputation (reproducible packets)

Organizing all AI workloads in a facility into discrete, reproducible units, so that a verifier can recompute a random sample and check each one. ([Whole-workload recomputation (reproducible packets)](https://trustbutveri.fyi/mechanisms/reproducible-computation-packets/))

- Assessment: mechanism family.
- Development: Proposed (legacy code R1), assessed for recomputing whole workloads to show a cluster runs only declared inference.
- Security evidence: No published adversarial analysis recorded. Independent evaluation: unassessed. Formal proof: unassessed. Deployment assurance: unassessed.
- Claims in this proposal: none of them.
- Threat model: adversarial prover. Hardware: retrofit device. Prover cooperation: required. Attack testing: none. Category: Isolation & system architectures.
- What the verifier sees: model weights depends; inputs and outputs depends; training data depends. Recomputing sampled units needs weights and sampled inputs or training data inside the checking environment. The design depends on securing that environment; disclosure depends on its confidentiality boundary.
- Filter conflict: Attack testing is none; the filter asks for at least published analysis.

### Deterministic and bit-exact inference

Making model inference reproducible bit for bit, so that a verifier's re-run must match the provider's output exactly rather than approximately. ([Deterministic and bit-exact inference](https://trustbutveri.fyi/mechanisms/deterministic-inference/))

- Assessment: mechanism family.
- Development: Operational use (legacy code R3), assessed for reproducing open-model inference from receipts in Gensyn's information-market service.
- Security evidence: Published security analysis. Independent evaluation: unassessed. Formal proof: unassessed. Deployment assurance: unassessed.
- Claims in this proposal: none of them.
- Threat model: adversarial prover. Hardware: none. Prover cooperation: required. Attack testing: analysis. Category: Cryptographic & computational.
- What the verifier sees: model weights depends; inputs and outputs depends; training data not involved. Exact replay needs the weights, configuration and replayed requests inside the recomputation environment. What the verifier sees depends on whether that environment keeps them confidential.


## Properties

**Operational use**

- Deterministic and bit-exact inference: Operational use (legacy code R3), assessed for reproducing open-model inference from receipts in Gensyn's information-market service

**Built for an adversarial prover**

- Network taps and certifiers
- Deterministic and bit-exact inference

**No new hardware needed**

- Deterministic and bit-exact inference

**Failures since mitigated**

- Verifier dictionary attacks on hashes (in Network taps and certifiers) [1]

Not counted as properties, because the filters exclude them: Whole-workload recomputation (reproducible packets).


## Attack testing

Attack testing records published testing for this use. It does not by itself show independent review, a formal proof or that a deployed system is secure.

**Testing history**

- Network taps and certifiers: Analysis
- Deterministic and bit-exact inference: Analysis


## Limits

**Excluded by the filters**

- Whole-workload recomputation (reproducible packets): Attack testing is none; the filter asks for at least published analysis.

**Open significant failures**

- Output nondeterminism leaves covert capacity (known failure, theoretical argument, in Network taps and certifiers; https://trustbutveri.fyi/mechanisms/network-taps-and-certifiers/evidence/flaws/1/) [1][9]. Hashing cannot remove information hidden in the outputs themselves. The Secure Gateway Device paper estimates that about 0.1 bit per token remains even with seed-synchronized replay checks. For a 200k-GPU inference cluster at full load (2,000 tokens per GPU per second), that is about 40 Mbit/s of covert egress, enough to move a 1 TB model in under three days. The paper names this the core remaining challenge and points to deterministic replay or active scrubbing of hardware-induced entropy. An independent study found that an adversary who chooses the prompts roughly doubles the bits leaked per token under Gumbel-based inference verification; see Bounding unexplained information in outputs.

  Related mechanism: Deterministic and bit-exact inference (R3, in the proposal). Deterministic replay is one of the two remedies the flaw's source names.

  Related mechanism: Bounding unexplained information in outputs (R2, not in the proposal). Bounds the hidden information outputs can carry by measuring what the declared computation fails to predict.
- Residual side channels in simple passive setups (known failure, theoretical argument, in Network taps and certifiers; https://trustbutveri.fyi/mechanisms/network-taps-and-certifiers/evidence/flaws/4/) [5]. Amodo's analysis of its own tapped prototype lists unvalidated header fields, timing of permitted traffic and variation in response formatting as residual channels, and concludes that the passive tap must be replaced by an active one.

**Scope limitations**

- Some links cannot be passively tapped (scope limitation, open question, in Network taps and certifiers; https://trustbutveri.fyi/mechanisms/network-taps-and-certifiers/evidence/flaws/2/) [2][10]. Cankaya notes that copper-connected scale-up domains (for example NVL72 racks and TPU v7 cubes) are much harder to tap than fibre, and that optical budgets make passive taps impractical on 400GBASE-SR8 multimode links. Amodo found no taps advertised for 53 GBaud links as of May 2026.
- Encrypted fabrics hide plaintext from both parties (scope limitation, open question, in Network taps and certifiers; https://trustbutveri.fyi/mechanisms/network-taps-and-certifiers/evidence/flaws/3/) [2]. Cankaya notes that with TEE-protected sessions whose keys are ephemeral and managed inside the TEE, neither the operator nor the manufacturer can recover session keys after the session, so tapped traffic could not be opened for recomputation. For other encrypted fabrics, the operator can retain keys.
- Completeness rests on physical monitoring left out of scope (scope limitation, open question, in Network taps and certifiers; https://trustbutveri.fyi/mechanisms/network-taps-and-certifiers/evidence/flaws/5/) [1]. The Secure Gateway Device paper assumes the facility is physically monitored, and states that the whole architecture depends on the device being the only communication channel. It names radio emanation, power-line signalling and thermal channels as covert channels beyond that scope.

  Related mechanism: Side-channel suppression for isolated facilities (R1, not in the proposal). Addresses the radio, power-line and thermal channels that network-level designs leave out.
- Spare compute is outside the scheme (scope limitation, theoretical argument, in Whole-workload recomputation (reproducible packets); https://trustbutveri.fyi/mechanisms/reproducible-computation-packets/evidence/flaws/1/) [4][11]. The plan states that it does not verify that spare compute is not used for unapproved workloads, because this seems very challenging. Recomputation checks the correctness of declared work, not its completeness.

  Related mechanism: Proofs of useful work for capacity accounting (R1, not in the proposal). Proposed as one input to accounting for spare capacity on declared hardware.
- Some kernels remain genuinely nondeterministic (scope limitation, open question, in Deterministic and bit-exact inference; https://trustbutveri.fyi/mechanisms/deterministic-inference/evidence/flaws/1/) [16]. The bit-exact work separates kernels that are deterministic but not batch-invariant from truly nondeterministic ones that use atomic functions. Some integer de-quantization kernels use atomic additions and remain nondeterministic, so exact replay needs backends that avoid them.
- Cross-hardware replay relies on reverse-engineered, closed behaviour (scope limitation, open question, in Deterministic and bit-exact inference; https://trustbutveri.fyi/mechanisms/deterministic-inference/evidence/flaws/2/) [16][18]. Emulating one GPU's rounding on another requires reverse-engineering tensor-core arithmetic and modelling proprietary kernel choices. Hawkeye covers a subset of NVIDIA architectures and states that attention and other higher-level operations need further reverse engineering. For the bit-exact emulator, a proprietary Hopper kernel family is an open edge case.

**Open questions**

- Non-compliant work could be encoded inside compliant-looking packets (open question, theoretical argument, in Whole-workload recomputation (reproducible packets); https://trustbutveri.fyi/mechanisms/reproducible-computation-packets/evidence/flaws/2/) [4]. The plan notes that an AI company might try to encode a non-compliant workload inside a workload that looks compliant on the surface.

**Not yet demonstrated**

- Network taps and certifiers: Proposed (legacy code R1), assessed for committing a complete record of cluster traffic, so declared inference can be checked
- Whole-workload recomputation (reproducible packets): Proposed (legacy code R1), assessed for recomputing whole workloads to show a cluster runs only declared inference


## Possible additions

Mechanisms on the map, not in the proposal, that the records connect to an unaddressed or partly addressed claim, an open failure or a dependency. Pointers, not recommendations: each brings its own readiness level and findings, and none is claimed to close a failure.

- **Bounding unexplained information in outputs** (Research demonstration (legacy code R2), assessed for bounding how much hidden information can leave in checked inference outputs)
  - Bears on the open significant failure "Output nondeterminism leaves covert capacity" in Network taps and certifiers. Bounds the hidden information outputs can carry by measuring what the declared computation fails to predict.
- **Tamper evidence for verifier devices** (Research demonstration (legacy code R2), assessed for detecting probing of proposed verifier hardware, using server and electronics prototypes as evidence)
  - Network taps and certifiers waits on it: Taps and gateway devices need tamper-evident housing and physical monitoring so that traffic cannot bypass them.
- **Side-channel suppression for isolated facilities** (Proposed (legacy code R1), assessed for bounding physical covert channels out of a verified enclosure)
  - Network taps and certifiers waits on it: Radio, power-line and thermal channels are not addressed by network-level designs.
- **Sampled inference recomputation** (Operational use (legacy code R3), assessed for checking untrusted workers' activations against the declared model, prompt and precision)
  - Network taps and certifiers depends on it.


## Dependencies

**Missing prerequisites**

- Sampled inference recomputation (Operational use (legacy code R3), assessed for checking untrusted workers' activations against the declared model, prompt and precision), needed by Network taps and certifiers
- Tamper evidence for verifier devices (Research demonstration (legacy code R2), assessed for detecting probing of proposed verifier hardware, using server and electronics prototypes as evidence), needed by Network taps and certifiers
- Side-channel suppression for isolated facilities (Proposed (legacy code R1), assessed for bounding physical covert channels out of a verified enclosure), needed by Network taps and certifiers

**Shared foundations**

- Deterministic and bit-exact inference, relied on by Network taps and certifiers and Whole-workload recomputation (reproducible packets) (in the proposal)

**Blockers**

- Network taps and certifiers: No complete verification tap has been demonstrated at production frontend link rates, and on the tested CPU no hash algorithm reached line rate with minimum-size frames. (performance & compatibility) [10][26]
- Network taps and certifiers: Nondeterministic inference leaves covert capacity in outputs that hashing cannot remove. (evidence binding; waits on Deterministic and bit-exact inference) [1]
- Network taps and certifiers: Taps and gateway devices need tamper-evident housing and physical monitoring so that traffic cannot bypass them. (hardware trust; waits on Tamper evidence for verifier devices) [1][3]
- Network taps and certifiers: Radio, power-line and thermal channels are not addressed by network-level designs. (coverage & hidden compute; waits on Side-channel suppression for isolated facilities) [1]
- Network taps and certifiers: Red-teaming by specialists is called for but has not been reported. (adversarial validation) [1]
- Whole-workload recomputation (reproducible packets): Workloads are not reproducible by default, and achieving reproducibility may cost performance. (performance & compatibility; waits on Deterministic and bit-exact inference) [4]
- Whole-workload recomputation (reproducible packets): Network packets are not individually reproducible by default; making them so may need considerable software, firmware and hardware work. Amodo rates this 'not on track'. (performance & compatibility) [13]
- Whole-workload recomputation (reproducible packets): All traffic must reach the recomputation server via network taps, and the server's integrity is critical. (hardware trust; waits on Network taps and certifiers) [4][13]
- Whole-workload recomputation (reproducible packets): Recomputing training steps needs checkpoints: writing one at every step would cost more than 100% overhead, so Amodo's design needs a spare data-parallel replica that tracks the weights instead. (performance & compatibility) [11]
- Deterministic and bit-exact inference: Batch-invariant kernels cost throughput: in Thinking Machines' Qwen3-8B test, an improved deterministic build took 42 s against 26 s for vLLM's default, and SGLang reports an average 34.35% slowdown on its FlashInfer and FlashAttention 3 backends. (performance & compatibility) [17][19]
- Deterministic and bit-exact inference: Coverage is incomplete: the bit-exact emulator targets dense blocks on NVIDIA GPUs and excludes mixture-of-experts inference and training, and vLLM's batch-invariant mode is in beta, with open work on AMD hardware and speculative decoding. (performance & compatibility) [16][20][27]
- Deterministic and bit-exact inference: Amodo's status page for the AI 2040 verification plan rates a reproducible inference stack for that plan as 'not started'. (performance & compatibility) [13]
- Deterministic and bit-exact inference: Exact replay requires the prover to disclose weights, software versions, parallelism and batch sizes to whoever recomputes. (privacy & leakage) [3][16]


## What the verifier sees

- Model weights: shown by none; depends on the design for Network taps and certifiers, Whole-workload recomputation (reproducible packets) and Deterministic and bit-exact inference; hidden by none; not involved in none; unspecified for none.
- Inputs and outputs: shown by none; depends on the design for Network taps and certifiers, Whole-workload recomputation (reproducible packets) and Deterministic and bit-exact inference; hidden by none; not involved in none; unspecified for none.
- Training data: shown by none; depends on the design for Network taps and certifiers and Whole-workload recomputation (reproducible packets); hidden by none; not involved in Deterministic and bit-exact inference; unspecified for none.

## Implementations

- Network taps and certifiers: [AI 2040 inference-only verification stack](https://trustbutveri.fyi/implementations/ai-2040-inference-only-verification-plan/) (R1, proposed architecture); [Low-trust AI compute verification system overview](https://trustbutveri.fyi/implementations/low-trust-compute-verification-system-overview/) (R1, proposed architecture); [SASH confidential network logger](https://trustbutveri.fyi/implementations/sash-confidential-network-logger/) (R1, research prototype)
- Whole-workload recomputation (reproducible packets): [AI 2040 inference-only verification stack](https://trustbutveri.fyi/implementations/ai-2040-inference-only-verification-plan/) (R1, proposed architecture)
- Deterministic and bit-exact inference: [Batch-invariant inference kernels (Thinking Machines)](https://trustbutveri.fyi/implementations/batch-invariant-inference-kernels/) (R2, open-source project); [Verde and RepOps (Gensyn)](https://trustbutveri.fyi/implementations/gensyn-verde-repops/) (R3, product); [Low-trust AI compute verification system overview](https://trustbutveri.fyi/implementations/low-trust-compute-verification-system-overview/) (R1, proposed architecture)

## Sources

1. Fingerprinting All AI Cluster I/O Without Mutually Trusted Processors, N. Cankaya et al. (2026). https://arxiv.org/abs/2606.10724
2. The Fundamentals and Feasibility of Secure Network Taps for Verifying AI Datacenter Use, N. Cankaya (2026). https://nacicankaya.substack.com/p/research-note-the-fundamentals-and
3. A System Overview for Near-Term, Low-Trust AI Compute Verification, N. Cankaya (2026). https://intelligence.org/wp-content/uploads/2026/06/A-system-overview-for-near-term-low-trust-AI-compute-verification.pdf
4. Verification Plan, R. Dean (2026). https://ai-2040.com/supplements/verification-plan
5. Fitting a Network TAP to our Inference Verification Prototype, Amodo Design (2026). https://amododesign.com/notes/2026-09-15-network-tap-inference-verification/
6. Amodo-Design/Inference-Recomputation-Prototype (GitHub repository), Amodo Design (2026). https://github.com/Amodo-Design/Inference-Recomputation-Prototype
7. inference-verification: Inference Verification Prototype, Singapore AI Safety Hub (SASH) (2026). https://github.com/sg-ai-safety-hub/inference-verification
8. Internationalising AI Verification, Singapore AI Safety Hub (SASH) (2026). https://www.aisafety.sg/research/internationalising-ai-verification
9. Adversarial Entropy Inflation Against Gumbel-Based Inference Verification, N. Kezins (2026). https://arxiv.org/abs/2608.23375
10. Network Tapping for AI Verification: A Technical Assessment, Amodo Design (2026). https://amododesign.com/notes/2026-05-03-network-tapping/
11. Example Schemes for Verifying High-Stakes AI Agreements, Amodo Design (2026). https://amododesign.com/notes/2026-06-23-verification-algorithms/
12. Scaling Recomputation Inference Verification, Amodo Design (2026). https://amododesign.com/notes/2026-09-02-scaling-recomputation-inference-verification/
13. AI 2040 Plan A — Verification SITREP, Amodo Design (2026). https://amododesign.com/ai-verification/plan-a-sitrep/
14. Get Involved in Verification, AI Futures Project (2026). https://ai-2040.com/supplements/verification-plan/get-involved
15. Proof-of-Learning is Currently More Broken Than You Think, C. Fang et al. (2023). https://arxiv.org/abs/2208.03567
16. Bit-Exact AI Inference Verification Without Performance Tradeoffs, N. Cankaya (2026). https://arxiv.org/abs/2606.00279
17. Defeating Nondeterminism in LLM Inference, H. He & Thinking Machines Lab (2025). https://thinkingmachines.ai/blog/defeating-nondeterminism-in-llm-inference/
18. Hawkeye: Reproducing GPU-Level Non-Determinism, E. Badash et al. (2026). https://proceedings.mlsys.org/paper_files/paper/2026/hash/e217c271a57c365a246b0ad39e668ba8-Abstract-Conference.html
19. Towards Deterministic Inference in SGLang and Reproducible RL Training, The SGLang Team (2025). https://www.lmsys.org/blog/2025-09-22-sglang-deterministic/
20. Batch Invariance (vLLM documentation), vLLM project (2026). https://github.com/vllm-project/vllm/blob/main/docs/features/batch_invariance.md
21. gensyn-ai/ree: Gensyn Reproducible Execution Environment (GitHub repository), Gensyn (2026). https://github.com/gensyn-ai/ree
22. EigenCloud Brings Verifiable AI to Mass Market with EigenAI and EigenCompute Launches, EigenCloud (2025). https://www.eigenlabs.org/blog/eigencloud-brings-verifiable-ai-to-mass-market-with-eigenai-and-eigencompute-launches/
23. Building Delphi: Pricing, Settlement, and Agentic Trading, D. Jedamski (2026). https://www.gensyn.ai/blog/building-delphi-pricing-settlement-and-agentic-trading
24. Reproducible Execution Environment (REE) (Gensyn documentation), Gensyn (2026). https://docs.gensyn.ai/tech
25. What is Delphi? (Delphi documentation), Gensyn (2026). https://docs.delphi.fyi/
26. Network Traffic Hashing, Amodo Design (2026). https://amododesign.com/notes/2026-07-03-network-traffic-hashing/
27. [Feature]: Batch Invariant Feature and Performance Optimization (vLLM issue #27433), vLLM project contributors (2025). https://github.com/vllm-project/vllm/issues/27433