# 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-0019,M-0020&hide=weights 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. - **Keep hidden from the verifier: model weights.** Removes mechanisms that show the asset to the verifier. Conditional or unspecified exposure stays with a note and needs checking against the privacy requirement. Model weights: the checked model's parameters. Inputs and outputs: the requests a deployed model serves and its responses. Training data: what a model was trained on. Each mechanism's exposure is the editors' reading of its record: shown, depends on the design (kept, with a note), hidden, not involved, or unspecified for a selected implementation. Code and configuration are not covered yet. 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 | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | Chip registries and manufacturing records | Proposed | Published security analysis | Semi-trusted | Analysis | Existing features | 0 / 1 / 0 | not involved | not involved | not involved | | Remote detection of data centres | Proposed | Published security analysis | Adversarial | Analysis | None | 0 / 0 / 0 | not involved | not involved | not involved | ## Claims No claims chosen. ## Mechanisms ### 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. - Development: Proposed (legacy code R1), assessed for a checkable record of which chips were made and who declared owning them. - Security evidence: Published security analysis. Independent evaluation: unassessed. Formal proof: unassessed. Deployment assurance: unassessed. - 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. ### 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. - Development: Proposed (legacy code R1), assessed for finding undeclared data centres above an agreed compute threshold. - 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: 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** - Remote detection of data centres **No new hardware needed** - Chip registries and manufacturing records - Remote detection of data centres ## 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** - Chip registries and manufacturing records: Analysis - Remote detection of data centres: Analysis ## Limits **Open significant failures** - Documents and serial numbers can be forged (known failure, theoretical argument, in Chip registries and manufacturing records; https://trustbutveri.fyi/mechanisms/chip-registries-and-manufacturing-records/evidence/flaws/2/) [2]. 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. **Scope limitations** - Records cover only chips that were recorded (scope limitation, theoretical argument, in Chip registries and manufacturing records; https://trustbutveri.fyi/mechanisms/chip-registries-and-manufacturing-records/evidence/flaws/1/) [3][5]. 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. - Insiders could alter records before they are fixed (scope limitation, theoretical argument, in Chip registries and manufacturing records; https://trustbutveri.fyi/mechanisms/chip-registries-and-manufacturing-records/evidence/flaws/3/) [3]. 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. - Facilities can be disguised or hidden (scope limitation, theoretical argument, in Remote detection of data centres; https://trustbutveri.fyi/mechanisms/remote-detection-of-data-centres/evidence/flaws/1/) [5]. 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 (scope limitation, theoretical argument, in Remote detection of data centres; https://trustbutveri.fyi/mechanisms/remote-detection-of-data-centres/evidence/flaws/2/) [5][6]. 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. **Open questions** - Search for unknown sites is undemonstrated (open question, open question, in Remote detection of data centres; https://trustbutveri.fyi/mechanisms/remote-detection-of-data-centres/evidence/flaws/3/) [6]. 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 registries and manufacturing records: Proposed (legacy code R1), assessed for a checkable record of which chips were made and who declared owning them - Remote detection of data centres: Proposed (legacy code R1), 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 failure or a dependency. Pointers, not recommendations: each brings its own readiness level and findings, and none is claimed to close a failure. None found. ## Dependencies **Blockers** - Chip registries and manufacturing records: No implementation of an AI chip registry has been publicly reported, and covering re-exports would need cooperation from re-exporters and foreign governments that may not be feasible everywhere. (access & governance) [1][2] - Chip registries and manufacturing records: Linking records to physical chips needs hard-to-spoof unique IDs and inspections. (hardware trust) [1][2][3] - Chip registries and manufacturing records: Chips produced before a registry starts must be reconstructed from supplier records. (coverage & hidden compute) [3][5] - Remote detection of data centres: Wide-area, automated detection of data centres is not yet practical and needs large training datasets. (coverage & hidden compute) [6] - Remote detection of data centres: No measured detection or false-alarm rates for finding undeclared facilities have been published. (adversarial validation) [5][6] - Remote detection of data centres: Recent high-resolution imagery is costly, is limited by weather and needs trained analysts. (access & governance) [6] ## What the verifier sees - Model weights: shown by none; depends on the design for none; hidden by none; not involved in Chip registries and manufacturing records 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 registries and manufacturing records 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 registries and manufacturing records and Remote detection of data centres; unspecified for none. ## Implementations - Chip registries and manufacturing records: none on the map - Remote detection of data centres: none on the map ## Sources 1. 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 2. 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 3. 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 4. Hardware-Level Governance of AI Compute: A Feasibility Taxonomy for Regulatory Compliance and Treaty Verification, S. Ansari (2026). https://arxiv.org/abs/2604.04712 5. Covert AI Projects, B. Halstead & T. Larsen (2026). https://ai-2040.com/supplements/covert-ai-projects 6. Tracking Hyperscale AI Data Center Growth with Satellite Imagery, C. Krawec (2026). https://fas.org/publication/tracking-hyperscale/ 7. Introducing the Frontier Data Centers Hub, Epoch AI (2025). https://epoch.ai/latest/introducing-the-frontier-data-centers-hub 8. AI Data Centers Documentation – Methodology, Epoch AI (2026). https://epoch.ai/data/data-centers-documentation/methodology