# 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-0020,M-0019&chips=existing

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.

- **Chips: Existing chips only.** "Existing chips only" removes mechanisms that need changes to future chip designs. New chip features take years to reach a deployed fleet and cover only chips made after they ship. Mechanisms that use shipping features, such as trusted execution environments or performance counters, stay.

23 of 25 mechanisms on the map pass these filters.

## 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 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Remote detection of data centres | R1 | Adversarial | Analysis | None | 0 / 3 / 0 | not involved | not involved | not involved |
| Chip registries and manufacturing records | R1 | Semi-trusted | Analysis | Existing features | 0 / 3 / 0 | not involved | not involved | not involved |

## Claims

No claims chosen.

## Mechanisms

### Remote detection of data centres

Remote detection locates large data centres and estimates their power capacity without site access, using satellite imagery, heat signatures and public records such as permits. ([Remote detection of data centres](https://trustbutveri.fyi/mechanisms/remote-detection-of-data-centres/))

- Assessment: mechanism family.
- Readiness: R1 Proposed, assessed for finding undeclared data centres above an agreed compute threshold.
- Claims in this proposal: none of them.
- Threat model: adversarial prover. Hardware: none. Prover cooperation: not required. Attack testing: analysis. Category: Remote & side-channel sensing.
- What the verifier sees: model weights not involved; inputs and outputs not involved; training data not involved. Works from outside the facility; it does not handle model data.

### Chip registries and manufacturing records

Recording each AI chip's identity and owner from the fab onwards, and cryptographically fixing manufacturing records, so that chips can be accounted for later. ([Chip registries and manufacturing records](https://trustbutveri.fyi/mechanisms/chip-registries-and-manufacturing-records/))

- Assessment: mechanism family.
- Readiness: R1 Proposed, assessed for a checkable record of which chips were made and who declared owning them.
- Claims in this proposal: none of them.
- Threat model: semi-trusted prover. Hardware: existing features. Prover cooperation: required. Attack testing: analysis. Category: Compute accounting & provenance.
- What the verifier sees: model weights not involved; inputs and outputs not involved; training data not involved. Records chip identities and owners; it does not handle model data.


## Properties

**Built for an adversarial prover**

- Remote detection of data centres

**No new hardware needed**

- Remote detection of data centres
- Chip registries and manufacturing records


## 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**

- Remote detection of data centres: Analysis
- Chip registries and manufacturing records: Analysis


## Limits

**Open significant flaws**

- Facilities can be disguised or hidden (theoretical argument, in Remote detection of data centres; https://trustbutveri.fyi/mechanisms/remote-detection-of-data-centres/#flaw-1) [1]. Halstead and Larsen discuss two ways to hide a facility. One is to disguise it as a legitimate industrial site. The other is to build it underground, with cooling that avoids visible heat plumes. They note that the underground option requires bespoke engineering.
- Small sites may not be detectable (theoretical argument, in Remote detection of data centres; https://trustbutveri.fyi/mechanisms/remote-detection-of-data-centres/#flaw-2) [1][3]. Halstead and Larsen conclude that a sufficiently small covert project could not be ruled out with confidence. In their estimates, the chance of detection is lower for smaller sites. Krawec notes that small data centres in existing buildings may lack the distinctive features of large facilities.

  Related mechanism: Chip registries and manufacturing records (R1, in the proposal). Accounts for chips from the fab onwards, which does not depend on a site being visible.
- Search for unknown sites is undemonstrated (open question, in Remote detection of data centres; https://trustbutveri.fyi/mechanisms/remote-detection-of-data-centres/#flaw-3) [3]. Krawec reports that telling data centres apart from other industrial facilities systematically is difficult. Automating detection would need large amounts of training imagery and a purpose-trained model. In Krawec's words, automated data-centre detection "remains primarily conceptual at present".
- Records cover only chips that were recorded (theoretical argument, in Chip registries and manufacturing records; https://trustbutveri.fyi/mechanisms/chip-registries-and-manufacturing-records/#flaw-1) [1][7]. A registry or commitment accounts only for chips entered into it. Cankaya asks how a verifier would know it had found all chips, or how much "dark compute" remains, and notes that a fraudulent original record would mean unregistered chips had been made in advance. Halstead and Larsen propose reconstructing earlier production by auditing upstream suppliers.

  Related mechanism: Remote detection of data centres (R1, in the proposal). Looks for large data centres that were never declared, which a registry cannot show.
- Documents and serial numbers can be forged (theoretical argument, in Chip registries and manufacturing records; https://trustbutveri.fyi/mechanisms/chip-registries-and-manufacturing-records/#flaw-2) [6]. Avellar and Grunewald note that export documents can be forged, that companies can hide information behind obscure corporate structures, and that it may be possible to forge serial numbers on chips and racks. They recommend cryptographic attestation of a powered-on chip as an extra check.
- Insiders could alter records before they are fixed (theoretical argument, in Chip registries and manufacturing records; https://trustbutveri.fyi/mechanisms/chip-registries-and-manufacturing-records/#flaw-3) [7]. Cankaya argues that insiders who can photograph process secrets could also tamper with production records. A commitment makes changes after publication detectable, but it cannot show that the records were accurate when committed.

**Not yet demonstrated**

- Remote detection of data centres: R1 Proposed, assessed for finding undeclared data centres above an agreed compute threshold
- Chip registries and manufacturing records: R1 Proposed, assessed for a checkable record of which chips were made and who declared owning them


## 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.

None found.



## Dependencies

**Blockers**

- Remote detection of data centres: Wide-area, automated detection of data centres is not yet practical and needs large training datasets. (coverage & hidden compute) [3]
- Remote detection of data centres: No measured detection or false-alarm rates for finding undeclared facilities have been published. (adversarial validation) [1][3]
- Remote detection of data centres: Recent high-resolution imagery is costly, is limited by weather and needs trained analysts. (access & governance) [3]
- Chip registries and manufacturing records: No AI chip registry operates, and covering re-exports would need cooperation from re-exporters and foreign governments that may not be feasible everywhere. (access & governance) [2][6]
- Chip registries and manufacturing records: Linking records to physical chips needs hard-to-spoof unique IDs and inspections. (hardware trust) [2][6][7]
- Chip registries and manufacturing records: Chips produced before a registry starts must be reconstructed from supplier records. (coverage & hidden compute) [1][7]


## What the verifier sees

- Model weights: shown by none; depends on the design for none; hidden by none; not involved in Remote detection of data centres and Chip registries and manufacturing records; unspecified for none.
- Inputs and outputs: shown by none; depends on the design for none; hidden by none; not involved in Remote detection of data centres and Chip registries and manufacturing records; unspecified for none.
- Training data: shown by none; depends on the design for none; hidden by none; not involved in Remote detection of data centres and Chip registries and manufacturing records; unspecified for none.

## Implementations

- Remote detection of data centres: none on the map
- Chip registries and manufacturing records: none on the map

## Sources

1. Covert AI Projects, B. Halstead & T. Larsen (2026). https://ai-2040.com/supplements/covert-ai-projects
2. Verifying International Agreements on AI: Six Layers of Verification for Rules on Large-Scale AI Development and Deployment, M. Baker et al. (2025). https://www.rand.org/pubs/working_papers/WRA4077-1.html
3. Tracking Hyperscale AI Data Center Growth with Satellite Imagery, C. Krawec (2026). https://fas.org/publication/tracking-hyperscale/
4. Introducing the Frontier Data Centers Hub, Epoch AI (2025). https://epoch.ai/latest/introducing-the-frontier-data-centers-hub
5. AI Data Centers Documentation – Methodology, Epoch AI (2026). https://epoch.ai/data/data-centers-documentation/methodology
6. Near-Term Verification Methods for AI Chip Exports, B. Avellar & E. Grunewald (2026). https://www.iaps.ai/research/near-term-verification-methods-for-ai-chip-exports
7. TSMC most definitely has a golden record of all AI chips it made, N. Cankaya (2025). https://nacicankaya.substack.com/p/tsmc-most-definitely-has-a-golden
8. Hardware-Level Governance of AI Compute: A Feasibility Taxonomy for Regulatory Compliance and Treaty Verification, S. Ansari (2026). https://arxiv.org/abs/2604.04712