# AI verification proposal

A proposal built with the Proposal Explorer of the AI Verification Tech Map (https://trustbutveri.fyi/), from its records of 2026-10-08. Interactive version: https://trustbutveri.fyi/explorer/?mechanisms=M-0015,M-0003&implementations=M-0015:I-0011

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 readiness and open flaws. Readiness levels R0 to R4 describe one record's public evidence for its assessed use and are never combined. Definitions: https://trustbutveri.fyi/about/methodology/ (roles, properties and flaws) and https://trustbutveri.fyi/about/readiness/ (readiness levels).

## Filters

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

None set. Every mechanism on the map was available.

## Overview

One row per mechanism, read from its record. Open flaws: critical / significant / minor. The last three columns are the editors' reading of what the verifier sees.

| Mechanism | Readiness | Prover | Attack testing | Hardware | Open flaws | Weights | Inputs and outputs | Training data |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Memory wiping and proofs of secure erasure / AI 2040 inference-only verification stack | R1 | Adversarial | None | Retrofit device | 0 / 3 / 0 | unspecified | unspecified | unspecified |
| Whole-workload recomputation (reproducible packets) | R1 | Adversarial | None | Retrofit device | 0 / 2 / 0 | depends | depends | depends |

## Claims

No claims chosen.

## Mechanisms

### Memory wiping and proofs of secure erasure

A proposed retrofit that isolates data-centre inference units, taps their front-end traffic and recomputes random samples to check that only declared inference runs. ([Memory wiping and proofs of secure erasure](https://trustbutveri.fyi/mechanisms/memory-wiping-and-secure-erasure/))

- Assessment: selected implementation [AI 2040 inference-only verification stack](https://trustbutveri.fyi/implementations/ai-2040-inference-only-verification-plan/).
- Readiness: R1 Proposed, assessed for showing that retrofitted data centres run only inference.
- 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 unspecified; inputs and outputs unspecified; training data unspecified. This Explorer has no asset-specific exposure assessment for this implementation. Check its source and deployment assumptions.

### 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.
- Readiness: R1 Proposed, assessed for recomputing whole workloads to show a cluster runs only declared inference.
- 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.


## Properties

**Built for an adversarial prover**

- Memory wiping and proofs of secure erasure
- Whole-workload recomputation (reproducible packets)


## Attack testing

Published attempts to break a system, including those that found failures. Testing history does not establish that open flaws are resolved.


## Limits

**Open significant flaws**

- The recomputation server must be trusted (theoretical argument, in AI 2040 inference-only verification stack; https://trustbutveri.fyi/implementations/ai-2040-inference-only-verification-plan/#flaw-1) [1][2][4]. The plan calls the integrity of the recomputation server an extremely important aspect, and its argument that sampling verifies all outputs assumes that the server's computations and outputs can be trusted. The companion page notes that the server sits inside the prover's facility, possibly under the prover's physical control, and that hardening it against integrity attacks needs significant research. Amodo rates recomputation-server security as not on track.
- Spare compute is not verified (theoretical argument, in AI 2040 inference-only verification stack; https://trustbutveri.fyi/implementations/ai-2040-inference-only-verification-plan/#flaw-2) [1]. The plan states that it does not verify that spare compute is unused for unapproved workloads, because this seems very challenging. It relies instead on side-channel bounds and memory wipes, so that the only results that persist are verified inference outputs.
- A recomputation family degrades against prompt-controlling adversaries (demonstrated attack, in AI 2040 inference-only verification stack; https://trustbutveri.fyi/implementations/ai-2040-inference-only-verification-plan/#flaw-3) [1][2][6][7]. The plan's companion page names DiFR among the recomputation schemes being tested. An independent study found that Gumbel-based inference verification, the family that includes Token-DiFR, leaks roughly twice as many bits per token when the adversary chooses prompts that disrupt the text's structure, across six models of 1 to 32 billion parameters. The slowdown it imposes on a hidden-signalling adversary falls from 146–254 times under benign prompts to 60–118 times. The attack weakens the bound on hidden information in outputs, which the plan relies on to keep undeclared results from leaving.
- Spare compute is outside the scheme (theoretical argument, in Whole-workload recomputation (reproducible packets); https://trustbutveri.fyi/mechanisms/reproducible-computation-packets/#flaw-1) [1][12]. 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.
- Non-compliant work could be encoded inside compliant-looking packets (theoretical argument, in Whole-workload recomputation (reproducible packets); https://trustbutveri.fyi/mechanisms/reproducible-computation-packets/#flaw-2) [1]. The plan notes that an AI company might try to encode a non-compliant workload inside a workload that looks compliant on the surface.

**Family finding context**

- Context for AI 2040 inference-only verification stack; applicability depends on the finding's scope. Memory the wipe cannot reach (open question, in Memory wiping and proofs of secure erasure; https://trustbutveri.fyi/mechanisms/memory-wiping-and-secure-erasure/#flaw-1) [8][9]. Amodo's inventory of a GB200 system lists many memory stores beyond GPU HBM and host DRAM. It notes that SSD controller DRAM sits on a private bus that host commands cannot read or write, and that its optimized algorithm leaves 25 GiB of HBM unattested. It also asks how switch memory could be wiped.
- Context for AI 2040 inference-only verification stack; applicability depends on the finding's scope. Outside help during challenges (theoretical argument, in Memory wiping and proofs of secure erasure; https://trustbutveri.fyi/mechanisms/memory-wiping-and-secure-erasure/#flaw-2) [10][11]. Classic proofs of secure erasure assume the device is isolated during the protocol. Bursuc et al. relax this to a bound on how close a helper can be, enforced by round-trip times. In data centres, remote memory access has round trips of about 1–2 µs, against about 70–200 ns for local DRAM. The MIRI overview therefore says verification depends on ruling out RDMA by latency or physical disconnection.

  Related mechanism: Timed challenge-response and memory-occupation challenges (R2, not in the proposal). Timed challenges bound how far away a helper can be by how quickly it must answer.

  Related mechanism: Bandwidth limits and compartmentalization (R2, not in the proposal). Removing or capping links between groups of accelerators limits remote memory access during a challenge.
- Context for AI 2040 inference-only verification stack; applicability depends on the finding's scope. Gap between erased and total memory (theoretical argument, in Memory wiping and proofs of secure erasure; https://trustbutveri.fyi/mechanisms/memory-wiping-and-secure-erasure/#flaw-3) [10]. Bursuc et al. note that memory left between the erased region and the device's full memory could hold data, and that their bounds are tighter only against a restricted adversary.

**Not yet demonstrated**

- Memory wiping and proofs of secure erasure: R1 Proposed, assessed for showing that retrofitted data centres run only inference
- Whole-workload recomputation (reproducible packets): R1 Proposed, 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 flaw or a dependency. Pointers, not recommendations: each brings its own readiness level and flaws, and none is claimed to close a flaw.

- **Proofs of useful work for capacity accounting** (R1 Proposed, assessed for bounding the spare capacity of declared hardware that could run training)
  - Bears on the open significant flaw "Spare compute is outside the scheme" in Whole-workload recomputation (reproducible packets). Proposed as one input to accounting for spare capacity on declared hardware.
- **Deterministic and bit-exact inference** (R3 In production, assessed for reproducing open-model inference from receipts in Gensyn's information-market service)
  - Whole-workload recomputation (reproducible packets) waits on it: Workloads are not reproducible by default, and achieving reproducibility may cost performance.
- **Tamper evidence for verifier devices** (R2 Demonstrated, assessed for detecting probing of proposed verifier hardware, using server and electronics prototypes as evidence)
  - Memory wiping and proofs of secure erasure waits on it: Checking that taps are correctly installed and stay in place at scale is not a solved problem, and hardening the recomputation server inside the prover's facility needs significant research.
- **Network taps and certifiers** (R1 Proposed, assessed for committing a complete record of cluster traffic, so declared inference can be checked)
  - Memory wiping and proofs of secure erasure waits on it: Passive optical taps work at 400G, but the 800G and 1600G line rates now arriving in data centres are undemonstrated.
  - Whole-workload recomputation (reproducible packets) waits on it: All traffic must reach the recomputation server via network taps, and the server's integrity is critical.
- **Side-channel suppression for isolated facilities** (R1 Proposed, assessed for bounding physical covert channels out of a verified enclosure)
  - Memory wiping and proofs of secure erasure waits on it: There is no plan yet for quickly scaling side-channel defences on a frontier cluster; only early theoretical pieces exist.
- **Sampled inference recomputation** (R3 In production, assessed for checking untrusted workers' activations against the declared model, prompt and precision)
  - Memory wiping and proofs of secure erasure depends on it.


## Dependencies

**Missing prerequisites**

- Sampled inference recomputation (R3 In production, assessed for checking untrusted workers' activations against the declared model, prompt and precision), needed by Memory wiping and proofs of secure erasure
- Deterministic and bit-exact inference (R3 In production, assessed for reproducing open-model inference from receipts in Gensyn's information-market service), needed by Whole-workload recomputation (reproducible packets)
- Network taps and certifiers (R1 Proposed, assessed for committing a complete record of cluster traffic, so declared inference can be checked), needed by Whole-workload recomputation (reproducible packets)

**Shared foundations**

- Network taps and certifiers, relied on by Memory wiping and proofs of secure erasure and Whole-workload recomputation (reproducible packets)

**Blockers**

- Memory wiping and proofs of secure erasure: A fully reproducible inference stack needs substantial software and tooling, and per-packet network reproducibility may need considerable software, firmware and possibly hardware work. (performance & compatibility; waits on Whole-workload recomputation (reproducible packets)) [2]
- Memory wiping and proofs of secure erasure: Passive optical taps work at 400G, but the 800G and 1600G line rates now arriving in data centres are undemonstrated. (performance & compatibility; waits on Network taps and certifiers) [2]
- Memory wiping and proofs of secure erasure: Checking that taps are correctly installed and stay in place at scale is not a solved problem, and hardening the recomputation server inside the prover's facility needs significant research. (hardware trust; waits on Tamper evidence for verifier devices) [2][4]
- Memory wiping and proofs of secure erasure: There is no plan yet for quickly scaling side-channel defences on a frontier cluster; only early theoretical pieces exist. (coverage & hidden compute; waits on Side-channel suppression for isolated facilities) [2]
- Memory wiping and proofs of secure erasure: Memory wiping may use existing algorithms, but hardware testing is at an early stage. (coverage & hidden compute; waits on Memory wiping and proofs of secure erasure) [2]
- Memory wiping and proofs of secure erasure: Robust red-teaming of recomputation schemes has not started, and most algorithm development remains academic. (adversarial validation) [2][4]
- 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) [1]
- 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) [4]
- 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) [1][4]
- 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) [12]


## What the verifier sees

- Model weights: shown by none; depends on the design for Whole-workload recomputation (reproducible packets); hidden by none; not involved in none; unspecified for Memory wiping and proofs of secure erasure.
- Inputs and outputs: shown by none; depends on the design for Whole-workload recomputation (reproducible packets); hidden by none; not involved in none; unspecified for Memory wiping and proofs of secure erasure.
- Training data: shown by none; depends on the design for Whole-workload recomputation (reproducible packets); hidden by none; not involved in none; unspecified for Memory wiping and proofs of secure erasure.

## Implementations

- Memory wiping and proofs of secure erasure: [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)
- Whole-workload recomputation (reproducible packets): [AI 2040 inference-only verification stack](https://trustbutveri.fyi/implementations/ai-2040-inference-only-verification-plan/) (R1, proposed architecture)

## Sources

1. Verification Plan, R. Dean (2026). https://ai-2040.com/supplements/verification-plan
2. Get Involved in Verification, AI Futures Project (2026). https://ai-2040.com/supplements/verification-plan/get-involved
3. Verifying international AI deals: Plan A, the state-of-play, and what you can do to help, T. Milton et al. (2026). https://amodo.substack.com/p/verifying-international-ai-deals
4. AI 2040 Plan A — Verification SITREP, Amodo Design (2026). https://amododesign.com/ai-verification/plan-a-sitrep/
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. Verifying LLM Inference to Detect Model Weight Exfiltration, R. Rinberg et al. (2025). https://arxiv.org/abs/2511.02620
7. Adversarial Entropy Inflation Against Gumbel-Based Inference Verification, N. Kezins (2026). https://arxiv.org/abs/2608.23375
8. Memory Wipes - Performance Analysis, Amodo Design (2026). https://amododesign.com/notes/2026-07-01-memory-wiping/
9. Improving Disk Wiping Speed for Memory Wipes, Amodo Design (2026). https://amododesign.com/notes/2026-09-14-disk-wiping-speed/
10. Software-Based Memory Erasure with Relaxed Isolation Requirements, S. Bursuc et al. (2024). https://ieeexplore.ieee.org/document/10664348/
11. 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
12. Example Schemes for Verifying High-Stakes AI Agreements, Amodo Design (2026). https://amododesign.com/notes/2026-06-23-verification-algorithms/
13. Scaling Recomputation Inference Verification, Amodo Design (2026). https://amododesign.com/notes/2026-09-02-scaling-recomputation-inference-verification/
14. Proof-of-Learning is Currently More Broken Than You Think, C. Fang et al. (2023). https://arxiv.org/abs/2208.03567