# 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-0018,M-0020&cols=tested

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 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Chip location verification | R1 | Adversarial | Analysis | Existing features | 0 / 4 / 0 | not involved | not involved | not involved |
| Remote detection of data centres | R1 | Adversarial | Analysis | None | 0 / 3 / 0 | not involved | not involved | not involved |

## Claims

No claims chosen.

## Mechanisms

### Chip location verification

Timing a chip's signed replies to trusted servers at known places, so that the speed of light bounds how far away the chip can be. ([Chip location verification](https://trustbutveri.fyi/mechanisms/chip-location-verification/))

- Assessment: mechanism family.
- Readiness: R1 Proposed, assessed for bounding how far a chip is from trusted landmark servers when checked.
- Claims in this proposal: none of them.
- Threat model: adversarial 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. Times signed replies from chips; it does not handle model data.

### 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.


## Properties

**Built for an adversarial prover**

- Chip location verification
- Remote detection of data centres

**No new hardware needed**

- Chip location verification
- Remote detection of data centres


## 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**

- Chip location verification: Analysis
- Remote detection of data centres: Analysis


## Limits

**Open significant flaws**

- Extracting a chip's key lets another device answer for it (theoretical argument, in Chip location verification; https://trustbutveri.fyi/mechanisms/chip-location-verification/#flaw-1) [1][6]. Ping-based protocols rely on cryptographic keys stored on the chip. Tee and Happel argue that an adversary with physical access could extract these keys and so compromise location verification. They propose GPU fingerprints as a mitigation, so far tested on 24 GPUs. Brass and Aarne assume the keys are stored securely, for example in a TPM.
- Added delay can shift an estimated position (demonstrated attack, in Chip location verification; https://trustbutveri.fyi/mechanisms/chip-location-verification/#flaw-2) [1][4]. Brass and Aarne cite internet-geolocation research in which artificially increased round-trip times moved the estimated location by up to 1,000 km, with a 74% chance of avoiding detection. Avellar and Grunewald list inflated ping times from circuitous routing as an evasion route. Added delay only loosens a distance bound, and Brass and Aarne propose a hard time limit as the counter: a chip that replies too slowly cannot be ruled out of a restricted location.
- Faster-than-assumed network paths (theoretical argument, in Chip location verification; https://trustbutveri.fyi/mechanisms/chip-location-verification/#flaw-3) [1][4]. Brass and Aarne list dark fibre and other private high-speed interconnects as ways to lower measured delays artificially. They judge that leasing dark fibre would probably not be a considerable challenge for covertly or openly adversarial actors. Avellar and Grunewald note that this can make a chip appear to be somewhere else entirely. A limit set at the vacuum speed of light cannot be beaten, but it makes honest chips fail more often.
- Compromised landmarks can falsify measurements (theoretical argument, in Chip location verification; https://trustbutveri.fyi/mechanisms/chip-location-verification/#flaw-4) [1][4][5]. A party that controls landmark servers can report false timing. Brass and Aarne cite research in which manipulating a third of the landmarks shifted the estimated location by about 700 km. Avellar and Grunewald note that compromised landmarks let adversaries spoof travel-time measurements directly. The draft specification asks verifiers to require anchors in diverse places, run by several independent operators.
- 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) [8]. 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) [8][10]. 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, not 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) [10]. 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".

**Not yet demonstrated**

- Chip location verification: R1 Proposed, assessed for bounding how far a chip is from trusted landmark servers when checked
- Remote detection of data centres: R1 Proposed, assessed for finding undeclared data centres above an agreed compute threshold


## 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.

- **Chip registries and manufacturing records** (R1 Proposed, assessed for a checkable record of which chips were made and who declared owning them)
  - Bears on the open significant flaw "Small sites may not be detectable" in Remote detection of data centres. Accounts for chips from the fab onwards, which does not depend on a site being visible.
- **TEE remote attestation for AI workloads** (R3 In production, assessed for showing which software ran to a party that distrusts the operator holding the hardware)
  - Chip location verification depends on it.


## Dependencies

**Missing prerequisites**

- TEE remote attestation for AI workloads (R3 In production, assessed for showing which software ran to a party that distrusts the operator holding the hardware), needed by Chip location verification

**Blockers**

- Chip location verification: No public code or reproducible end-to-end location results are available for the reported H100 prototype. (adversarial validation) [2][3]
- Chip location verification: Per-chip keys must be provisioned and protected against extraction; hardware-integrated, tamper-resistant versions still need R&D. (hardware trust) [1][6][13]
- Chip location verification: The time limit forces a trade-off: a limit at the speed of light in fibre can be beaten by faster links, while one at the vacuum speed of light makes honest chips fail often. (protocol soundness) [1]
- Chip location verification: A trusted landmark network must be built and secured, and who should operate it, under what oversight, is unsettled. (access & governance) [1][4]
- Remote detection of data centres: Wide-area, automated detection of data centres is not yet practical and needs large training datasets. (coverage & hidden compute) [10]
- Remote detection of data centres: No measured detection or false-alarm rates for finding undeclared facilities have been published. (adversarial validation) [8][10]
- Remote detection of data centres: Recent high-resolution imagery is costly, is limited by weather and needs trained analysts. (access & governance) [10]


## What the verifier sees

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

## Implementations

- Chip location verification: [Lucid sovereignty (location) certificates](https://trustbutveri.fyi/implementations/lucid-location-certificates/) (R1, standard)
- Remote detection of data centres: none on the map

## Sources

1. Location Verification for AI Chips, A. Brass & O. Aarne (2024). https://www.iaps.ai/research/location-verification-for-ai-chips
2. Location Verification for AI Chips (issue brief), A. Brass (2025). https://static1.squarespace.com/static/64edf8e7f2b10d716b5ba0e1/t/6827b67275666f3757f134ea/1747433075281/Location+Verification+two-pager.pdf
3. Ping-based Location, Ulyssean (2025). https://ping-location.info/
4. 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
5. Sovereignty Certificates: draft specification, version 0.1.0, Sovereignty Certificates Working Group (2025). https://github.com/Lucid-Computing/sovereignty-certificate-specification
6. GPU Fingerprinting for Location Verification, W. Tee & J. Happel (2026). https://arxiv.org/abs/2605.01930
7. Secure, Governable Chips: Using On-Chip Mechanisms to Manage National Security Risks from AI & Advanced Computing, O. Aarne et al. (2024). https://www.cnas.org/publications/reports/secure-governable-chips
8. Covert AI Projects, B. Halstead & T. Larsen (2026). https://ai-2040.com/supplements/covert-ai-projects
9. 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
10. Tracking Hyperscale AI Data Center Growth with Satellite Imagery, C. Krawec (2026). https://fas.org/publication/tracking-hyperscale/
11. Introducing the Frontier Data Centers Hub, Epoch AI (2025). https://epoch.ai/latest/introducing-the-frontier-data-centers-hub
12. AI Data Centers Documentation – Methodology, Epoch AI (2026). https://epoch.ai/data/data-centers-documentation/methodology
13. Hardware-Level Governance of AI Compute: A Feasibility Taxonomy for Regulatory Compliance and Treaty Verification, S. Ansari (2026). https://arxiv.org/abs/2604.04712