# 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-0001&implementations=M-0001:I-0001

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 | R1 | Adversarial | Analysis | None | 0 / 2 / 1 | not involved | not involved | not involved |
| Sampled inference recomputation / TOPLOC | R3 | Adversarial | Analysis | None | 0 / 4 / 0 | unspecified | unspecified | unspecified |

## Claims

No claims chosen.

## Mechanisms

### Memory wiping and proofs of secure erasure

Overwriting a device's memory in a way a verifier can check, so that data from earlier, undeclared work cannot persist in memory the wipe reaches. ([Memory wiping and proofs of secure erasure](https://trustbutveri.fyi/mechanisms/memory-wiping-and-secure-erasure/))

- Assessment: mechanism family.
- Readiness: R1 Proposed, assessed for showing that no data from earlier work persists in memory the wipe reaches.
- Claims in this proposal: none of them.
- Threat model: adversarial prover. Hardware: none. Prover cooperation: required. Attack testing: analysis. Category: Isolation & system architectures.
- What the verifier sees: model weights not involved; inputs and outputs not involved; training data not involved. Overwrites memory with verifier-chosen data; it does not handle model data.

### Sampled inference recomputation

TOPLOC is a hashing scheme from Prime Intellect that lets a verifier check whether an inference provider ran the model, prompt and precision it claims. ([Sampled inference recomputation](https://trustbutveri.fyi/mechanisms/sampled-inference-recomputation/))

- Assessment: selected implementation [TOPLOC](https://trustbutveri.fyi/implementations/toploc/).
- Readiness: R3 In production, assessed for checking that untrusted providers used the claimed model, prompt and precision.
- 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 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.


## Properties

**In production**

- Sampled inference recomputation: R3 In production, assessed for checking that untrusted providers used the claimed model, prompt and precision

**Built for an adversarial prover**

- Memory wiping and proofs of secure erasure
- Sampled inference recomputation

**No new hardware needed**

- Memory wiping and proofs of secure erasure
- Sampled inference recomputation


## Attack testing

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

**Testing history**

- Memory wiping and proofs of secure erasure: Analysis
- Sampled inference recomputation / TOPLOC: Analysis


## Limits

**Open significant flaws**

- 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) [5][6]. 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.
- 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) [3][4]. 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.
- Speculative decoding goes undetected (theoretical argument, in TOPLOC; https://trustbutveri.fyi/implementations/toploc/#flaw-1) [9]. The TOPLOC authors state that it cannot detect speculative decoding. In speculative decoding, a provider decodes with a cheaper model and uses the larger model only for prefill.
- Last-layer activations could be spoofed (open question, in TOPLOC; https://trustbutveri.fyi/implementations/toploc/#flaw-2) [9]. The TOPLOC authors name spoofing of the last hidden layer's activations as a potential attack. A provider could do this by pruning intermediate layers or by using a smaller model.
- Subtle modifications are harder to detect (open question, in TOPLOC; https://trustbutveri.fyi/implementations/toploc/#flaw-3) [9]. The TOPLOC authors state that large changes to the model or prompt are straightforward to detect, but subtle modifications are harder. In preliminary experiments, the margin separating fp8 from bf16 generation was small. The authors did not test whether TOPLOC distinguishes types of KV-cache compression.
- Tolerance leaves covert bandwidth (theoretical argument, in TOPLOC; https://trustbutveri.fyi/implementations/toploc/#flaw-4) [14]. TOPLOC accepts approximate matches. A check of this kind can put an upper bound on the covert bandwidth available to an adversary, but it cannot close that bandwidth. The limit applies to all statistical verification schemes.

**Family finding context**

- Context for TOPLOC; applicability depends on the finding's scope. Tolerance for numerical noise leaves a covert channel (demonstrated attack, in Sampled inference recomputation; https://trustbutveri.fyi/mechanisms/sampled-inference-recomputation/#flaw-1) [14][15][16]. Schemes that accept approximate matches can put an upper bound on an adversary's covert bandwidth, but they cannot close the channel. The weight-exfiltration detector cut exfiltratable information to under 0.5%, not to zero, on a 30-billion-parameter mixture-of-experts model under benign prompt traffic. Its authors called the channel's size under adversarial prompts an open empirical question. An independent study showed that an adversary who controls the prompts roughly doubles the bits leaked per token. Across six models, that cut the slowdown from 146–254 times under benign prompts to 60–118 times. The attack widens the exfiltration bound. It does not target the check that outputs match the declared model.

  Related mechanism: Deterministic and bit-exact inference (R3, not in the proposal). Bit-exact inference would remove the numerical tolerance if exact replay can be deployed with the required weights and configuration.
- Context for TOPLOC; applicability depends on the finding's scope. Only recorded traffic is checked (theoretical argument, in Sampled inference recomputation; https://trustbutveri.fyi/mechanisms/sampled-inference-recomputation/#flaw-2) [15][17]. Recomputation checks that recorded, declared workloads are correct. It cannot show that the record is complete. The published schemes do not cover hidden workloads run on the same compute, or substituted work. Rinberg et al. say their exfiltration-detection scheme cannot stand alone.

  Related mechanism: Network taps and certifiers (R1, not in the proposal). Taps copy and hash all traffic on the monitored links, which bears on whether the traffic record is complete. They do not show what else ran on the same chips.
- Context for TOPLOC; applicability depends on the finding's scope. Some inference optimizations are not covered (theoretical argument, in Sampled inference recomputation; https://trustbutveri.fyi/mechanisms/sampled-inference-recomputation/#flaw-3) [9][18]. TOPLOC's authors state that it cannot detect speculative decoding in which a cheaper model does the decoding. They did not test whether it distinguishes types of key-value (KV) cache compression. DiFR was evaluated only on sampling from a single model. Its authors sketch an extension to one speculative-decoding algorithm but do not test it.
- Context for TOPLOC; applicability depends on the finding's scope. Mixed hardware widens the honest baseline (open question, in Sampled inference recomputation; https://trustbutveri.fyi/mechanisms/sampled-inference-recomputation/#flaw-4) [18]. When honest reference runs span different GPU types, the spread of benign scores grows. In DiFR's tests on Qwen3-30B-A3B, pooling A100 and H200 runs left Token-DiFR unable to separate the two smallest tested changes, a temperature of 1.1 instead of 1.0 and a simulated top-2 sampling bug, at the target false-positive rate, while cross-entropy separated them. Matched provider and verifier environments, or pooling that weights rare large deviations, restored detection.

**Open minor flaws**

- 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) [3]. 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 no data from earlier work persists in memory the wipe reaches


## 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.

- **Bandwidth limits and compartmentalization** (R2 Demonstrated, assessed for monitoring inter-node traffic with operator-run software on four GPUs)
  - Bears on the open significant flaw "Outside help during challenges" in Memory wiping and proofs of secure erasure. Removing or capping links between groups of accelerators limits remote memory access during a challenge.
- **Timed challenge-response and memory-occupation challenges** (R2 Demonstrated, assessed for detecting whether a GPU is doing other work)
  - Bears on the open significant flaw "Outside help during challenges" in Memory wiping and proofs of secure erasure. Timed challenges bound how far away a helper can be by how quickly it must answer.
  - Memory wiping and proofs of secure erasure waits on it: Timed challenges must exclude remote memory and other helpers.


## Dependencies

**Missing prerequisites**

- Timed challenge-response and memory-occupation challenges (R2 Demonstrated, assessed for detecting whether a GPU is doing other work), needed by Memory wiping and proofs of secure erasure

**Blockers**

- Memory wiping and proofs of secure erasure: Wipes take time: tens of minutes for a pod's volatile memory and hours for SSDs, displacing work. (performance & compatibility) [4][5][6]
- Memory wiping and proofs of secure erasure: Timed challenges must exclude remote memory and other helpers. (coverage & hidden compute; waits on Timed challenge-response and memory-occupation challenges) [3][4]
- Memory wiping and proofs of secure erasure: All memory stores in a system must be inventoried and wiped at the same time. (coverage & hidden compute) [5]
- Sampled inference recomputation: No independent security evaluation has been published, and Amodo Design rates red-teaming of recomputation schemes as 'not started'. (adversarial validation) [19]
- Sampled inference recomputation: The verifier must run the model itself, which suits the paper's setting of providers serving open-weights models. (privacy & leakage) [9]


## What the verifier sees

- Model weights: shown by none; depends on the design for none; hidden by none; not involved in Memory wiping and proofs of secure erasure; unspecified for Sampled inference recomputation.
- Inputs and outputs: shown by none; depends on the design for none; hidden by none; not involved in Memory wiping and proofs of secure erasure; unspecified for Sampled inference recomputation.
- Training data: shown by none; depends on the design for none; hidden by none; not involved in Memory wiping and proofs of secure erasure; unspecified for Sampled inference recomputation.

## 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)
- Sampled inference recomputation: [AI 2040 inference-only verification stack](https://trustbutveri.fyi/implementations/ai-2040-inference-only-verification-plan/) (R1, proposed architecture); [DiFR (Divergence From Reference)](https://trustbutveri.fyi/implementations/difr/) (R2, research prototype); [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); [TOPLOC](https://trustbutveri.fyi/implementations/toploc/) (R3, open-source project)

## Sources

1. Verification Plan, R. Dean (2026). https://ai-2040.com/supplements/verification-plan
2. Secure Code Update for Embedded Devices via Proofs of Secure Erasure, D. Perito & G. Tsudik (2010). https://link.springer.com/chapter/10.1007/978-3-642-15497-3_39
3. Software-Based Memory Erasure with Relaxed Isolation Requirements, S. Bursuc et al. (2024). https://ieeexplore.ieee.org/document/10664348/
4. 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
5. Memory Wipes - Performance Analysis, Amodo Design (2026). https://amododesign.com/notes/2026-07-01-memory-wiping/
6. Improving Disk Wiping Speed for Memory Wipes, Amodo Design (2026). https://amododesign.com/notes/2026-09-14-disk-wiping-speed/
7. Amodo-Design/PoSE-Memory-Wiping (GitHub repository), Amodo Design (2026). https://github.com/Amodo-Design/PoSE-Memory-Wiping
8. Empirical Evaluation of Memory-Erasure Protocols, R. Gil-Pons et al. (2025). https://www.scitepress.org/Papers/2025/135548/135548.pdf
9. TOPLOC: A Locality Sensitive Hashing Scheme for Trustless Verifiable Inference, J. M. Ong et al. (2025). https://proceedings.mlr.press/v267/ong25a.html
10. PrimeIntellect-ai/toploc (GitHub repository), Prime Intellect (2025). https://github.com/PrimeIntellect-ai/toploc
11. INTELLECT-2: A Reasoning Model Trained Through Globally Decentralized Reinforcement Learning, Prime Intellect Team et al. (2025). https://arxiv.org/abs/2505.07291
12. SYNTHETIC-2, Prime Intellect (2025). https://www.primeintellect.ai/blog/synthetic-2
13. SYNTHETIC-2 Release: Four Million Collaboratively Generated Reasoning Traces, Prime Intellect (2025). https://www.primeintellect.ai/blog/synthetic-2-release
14. Bit-Exact AI Inference Verification Without Performance Tradeoffs, N. Cankaya (2026). https://arxiv.org/abs/2606.00279
15. Verifying LLM Inference to Detect Model Weight Exfiltration, R. Rinberg et al. (2025). https://arxiv.org/abs/2511.02620
16. Adversarial Entropy Inflation Against Gumbel-Based Inference Verification, N. Kezins (2026). https://arxiv.org/abs/2608.23375
17. Example Schemes for Verifying High-Stakes AI Agreements, Amodo Design (2026). https://amododesign.com/notes/2026-06-23-verification-algorithms/
18. DiFR: Inference Verification Despite Nondeterminism, A. Karvonen et al. (2025). https://arxiv.org/abs/2511.20621
19. AI 2040 Plan A — Verification SITREP, Amodo Design (2026). https://amododesign.com/ai-verification/plan-a-sitrep/