# 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-0004,M-0009,M-0020&hide=weights,training

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, training data.** 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 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Zero-knowledge proofs of inference | Research demonstration | Published attack testing | Adversarial | Independent red-team | None | 0 / 0 / 0 | hidden | shown | not involved |
| Hardware-enabled guarantees (flexHEG) and guarantee processors | Proposed | Published security analysis | Adversarial | Analysis | New chip design | 0 / 3 / 0 | hidden | hidden | hidden |
| 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

### Zero-knowledge proofs of inference

A prover produces a cryptographic proof that an output came from running a committed model on a given input, without revealing the weights. ([Zero-knowledge proofs of inference](https://trustbutveri.fyi/mechanisms/zk-proofs-of-inference/))

- Assessment: mechanism family.
- Development: Research demonstration (legacy code R2), assessed for proving a language model's output follows from committed weights, against a cheating prover.
- Security evidence: Published attack testing. Independent evaluation: unassessed. Formal proof: unassessed. Deployment assurance: unassessed.
- Claims in this proposal: none of them.
- Threat model: adversarial prover. Hardware: none. Prover cooperation: required. Attack testing: independent red-team. Category: Cryptographic & computational.
- What the verifier sees: model weights hidden; inputs and outputs shown; training data not involved. The weights stay committed and hidden; the verifier knows each input and output it checks.

### Hardware-enabled guarantees (flexHEG) and guarantee processors

A proposed add-on for AI chips: an auditable guarantee processor, sealed in a tamper-protected enclosure, that would check and enforce agreed rules on chip use. ([Hardware-enabled guarantees (flexHEG) and guarantee processors](https://trustbutveri.fyi/mechanisms/flexheg-guarantee-processors/))

- Assessment: mechanism family.
- Development: Proposed (legacy code R1), assessed for checking and enforcing training-compute limits on chips, against adversaries up to states.
- 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: new chip design. Prover cooperation: required. Attack testing: analysis. Category: On-chip & hardware-enabled.
- What the verifier sees: model weights hidden; inputs and outputs hidden; training data hidden. The guarantee processor sees the chip's traffic inside a sealed enclosure and reports only whether rules were kept.

### 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**

- Zero-knowledge proofs of inference
- Hardware-enabled guarantees (flexHEG) and guarantee processors
- Remote detection of data centres

**No new hardware needed**

- Zero-knowledge proofs of inference
- 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**

- Zero-knowledge proofs of inference: Independent red-team
- Hardware-enabled guarantees (flexHEG) and guarantee processors: Analysis
- Remote detection of data centres: Analysis


## Limits

**Open significant failures**

- State attackers can likely defeat current secure enclosures (known failure, theoretical argument, in Hardware-enabled guarantees (flexHEG) and guarantee processors; https://trustbutveri.fyi/mechanisms/flexheg-guarantee-processors/evidence/flaws/1/) [14][16]. The flexHEG authors write that "nation-state attackers can likely compromise the best current secure enclosures", and that the marginal cost of circumvention per device is hard to estimate. RAND similarly judges that anti-tamper measures "would not be insurmountable for a determined and well-resourced adversary", although they raise costs and can reveal tampering.
- Firmware-only retrofits rely on Secure Boot, which fault injection can bypass (known failure, theoretical argument, in Hardware-enabled guarantees (flexHEG) and guarantee processors; https://trustbutveri.fyi/mechanisms/flexheg-guarantee-processors/evidence/flaws/2/) [14]. Part II notes that the most common attack on Secure Boot replaces the firmware and applies a voltage glitch while the signature is being checked. It also notes that sophisticated actors may use microprobing or laser voltage probing to read key registers.
- FLOP accounting can be laundered through external data (known failure, theoretical argument, in Hardware-enabled guarantees (flexHEG) and guarantee processors; https://trustbutveri.fyi/mechanisms/flexheg-guarantee-processors/evidence/flaws/4/) [14]. Results of earlier or parallel workloads could be hidden in the "external data" fed to a device, which would falsify the total FLOP count unless the inputs are explained or time delays are imposed.

**Scope limitations**

- The proof covers a fixed-point approximation, not the floating-point model (scope limitation, open question, in Zero-knowledge proofs of inference; https://trustbutveri.fyi/mechanisms/zk-proofs-of-inference/evidence/flaws/1/) [1][5][7][11]. Current ZK inference systems prove a quantised version of the network. zkLLM scales values by 2^16 and reports small perplexity changes. Attestable reports quantising matrix multiplications to 8-bit integers while proving other operations in floating point. A verifier therefore learns about the proof-friendly variant, and must separately accept that this variant is the declared model. Trail of Bits built a ResNet-18 backdoor that is dormant in the full-precision model and active after ezkl's quantisation; whether it persists through proving was left for further investigation. A verification system design notes that ZKPs can emulate floating-point operations. Rounding makes floating-point results depend on summation order, so bit-for-bit replay of an accelerator's results needs its original reduction tree. The report calls emulating that tree inside a ZKP an open, intricate problem and asks what it would cost.
- A proof speaks only for the computations that were proven (scope limitation, theoretical argument, in Zero-knowledge proofs of inference; https://trustbutveri.fyi/mechanisms/zk-proofs-of-inference/evidence/flaws/2/) [12]. Attestable writes that "a proof of some computation is not a proof of all computation", and that a proof cannot discover a datacenter that was never declared. Proofs of inference do not by themselves show that no other workload ran on the same or other hardware.

  Related mechanism: Proofs of useful work for capacity accounting (R1, not in the proposal). The record names proof-of-work accounting as the kind of compute accounting needed to show that proven inference was the only work done.
- The model architecture is disclosed (scope limitation, theoretical argument, in Zero-knowledge proofs of inference; https://trustbutveri.fyi/mechanisms/zk-proofs-of-inference/evidence/flaws/3/) [1][3]. ZKML "requires that the model architecture (but not weights) is revealed", and zkLLM assumes a publicly known model structure. Architecture can be commercially sensitive.
- Proofs do not bind computational effort (Hollow-LLM) (scope limitation, demonstrated attack, in Zero-knowledge proofs of inference; https://trustbutveri.fyi/mechanisms/zk-proofs-of-inference/evidence/flaws/4/) [10]. Researchers at the University of Southern California show that a proof of inference certifies that an output is consistent with committed weights under the declared architecture, but not how much computation produced it. In their Hollow-LLM attack, a provider keeps the declared architecture and parameter count but commits to "ghost weights". Some layers pass their inputs through unchanged, and wide layers carry the signal in a small subspace, so a much smaller inner model does the real work. The ghost weights satisfy the verification circuit and yield valid proofs.

  The authors ran the attack with the proof procedure of zkGPT, a separate ZK inference system, on a 6-layer, 512-dimensional transformer declared as up to 12 layers and 1,024 dimensions. Outputs were identical to the inner model's, and serving cost stayed at the inner model's level. An honest model of the declared size cost 2.4 times as much to prefill and 3.1 times as much to decode. Proving cost still grew with the declared architecture.

  The authors note that results may be served before any proof, with the provider building the witness only when a call is selected for audit. They describe their constructions as "compatible with state-of-the-art zkLLM pipelines", and state that the attack does not imply a flaw in the proof system itself. They propose challenge-based audits and ablation tests, which raise the cost of cheating but give no guarantee.
- Many important rules cannot be checked on-chip (scope limitation, theoretical argument, in Hardware-enabled guarantees (flexHEG) and guarantee processors; https://trustbutveri.fyi/mechanisms/flexheg-guarantee-processors/evidence/flaws/3/) [13][15]. Malicious intent "is not a technical property observable on-chip", and misuse depends on what is done with a computation's results. A guarantee processor cannot easily tell whether a network is the whole system or one expert in a mixture-of-experts system. Part III judges that a fully local ruleset "may not be entirely feasible" for the same reason.
- Coverage stops at flexHEG-equipped chips (scope limitation, open question, in Hardware-enabled guarantees (flexHEG) and guarantee processors; https://trustbutveri.fyi/mechanisms/flexheg-guarantee-processors/evidence/flaws/6/) [13][15]. Motivated actors will always be able to use some compute that is not flexHEG-equipped. Recalling existing consumer GPUs would likely be impractical, and reaching perfect coverage, or conclusively proving that no secret government data centres exist, would be "practically quite difficult".

  Related mechanism: Chip registries and manufacturing records (R1, not in the proposal). Accounts for which chips exist and who holds them.

  Related mechanism: Remote detection of data centres (R1, in the proposal). Looks for undeclared facilities that hold other chips.
- 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/) [19]. 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/) [19][21]. 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.

**Open questions**

- Supply-chain diversion and hidden backdoors (open question, open question, in Hardware-enabled guarantees (flexHEG) and guarantee processors; https://trustbutveri.fyi/mechanisms/flexheg-guarantee-processors/evidence/flaws/5/) [14][15]. Components could be diverted before a guarantee processor is added, and backdoors could be introduced during design or manufacturing. Open-source designs and physical scans of randomly selected chips are proposed as countermeasures. Part III proposes international oversight of production and extensive testing of a random sample of finished devices.

  Related mechanism: Chip registries and manufacturing records (R1, not in the proposal). Records each chip's identity and owner from the fab onwards, which bears on diversion before a guarantee processor is fitted. It does not address hidden backdoors.
- 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/) [21]. 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**

- Hardware-enabled guarantees (flexHEG) and guarantee processors: Proposed (legacy code R1), assessed for checking and enforcing training-compute limits on chips, against adversaries up to states
- Remote detection of data centres: Proposed (legacy code R1), assessed for finding undeclared data centres above an agreed compute threshold

**Need new chip designs**

- Hardware-enabled guarantees (flexHEG) and guarantee processors


## 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.

- **Tamper evidence for verifier devices** (Research demonstration (legacy code R2), assessed for detecting probing of proposed verifier hardware, using server and electronics prototypes as evidence)
  - Hardware-enabled guarantees (flexHEG) and guarantee processors waits on it: State-level attackers who hold the hardware can likely compromise the best current secure enclosures.
- **Chip registries and manufacturing records** (Proposed (legacy code R1), assessed for a checkable record of which chips were made and who declared owning them)
  - Hardware-enabled guarantees (flexHEG) and guarantee processors waits on it: Governing all relevant chips depends on knowing where they are, through chip registries and detection of undeclared facilities.
- **Proofs of useful work for capacity accounting** (Proposed (legacy code R1), assessed for bounding the spare capacity of declared hardware that could run training)
  - Zero-knowledge proofs of inference waits on it: Showing that proven inference was the only work done needs a compute-accounting mechanism such as proof-of-work accounting, which is only proposed.
- **TEE remote attestation for AI workloads** (Operational use (legacy code R3), assessed for showing which software ran to a party that distrusts the operator holding the hardware)
  - Hardware-enabled guarantees (flexHEG) and guarantee processors depends on it.


## Dependencies

**Missing prerequisites**

- TEE remote attestation for AI workloads (Operational use (legacy code R3), assessed for showing which software ran to a party that distrusts the operator holding the hardware), needed by Hardware-enabled guarantees (flexHEG) and guarantee processors
- Chip registries and manufacturing records (Proposed (legacy code R1), assessed for a checkable record of which chips were made and who declared owning them), needed by Hardware-enabled guarantees (flexHEG) and guarantee processors

**Blockers**

- Zero-knowledge proofs of inference: Proving takes about 13 minutes (803 seconds) per 2,048-token forward pass of a 13B model on one A100, and a verification system design calls the overhead heavy. (performance & compatibility) [1][11]
- Zero-knowledge proofs of inference: ZKML and zkLLM prove fixed-point arithmetic, and a verification system design calls emulating an accelerator's original floating-point reduction tree inside a zero-knowledge proof, which bit-for-bit replay needs, an open and intricate problem whose cost is also unsettled. (performance & compatibility) [1][3][11]
- Zero-knowledge proofs of inference: zkLLM's code is unaudited, interactive and archived; the one audited ZK inference library, ezkl, had high-severity circuit soundness bugs before its fixes. (adversarial validation) [2][7]
- Zero-knowledge proofs of inference: Showing that proven inference was the only work done needs a compute-accounting mechanism such as proof-of-work accounting, which is only proposed. (coverage & hidden compute; waits on Proofs of useful work for capacity accounting) [12]
- Hardware-enabled guarantees (flexHEG) and guarantee processors: Integrated flexHEG needs substantial help from the accelerator manufacturer, and the authors estimate 3.7–7.9 years, from when the manufacturer starts work, for such hardware to displace other accelerators in frontier development. (access & governance) [14]
- Hardware-enabled guarantees (flexHEG) and guarantee processors: State-level attackers who hold the hardware can likely compromise the best current secure enclosures. (hardware trust; waits on Tamper evidence for verifier devices) [14][16]
- Hardware-enabled guarantees (flexHEG) and guarantee processors: Rival states would need to trust the design and manufacture of guarantee processors and enclosures, for example through open design, redundant processors from each side or oversight of production. (hardware trust) [13][15]
- Hardware-enabled guarantees (flexHEG) and guarantee processors: Restricting future rule updates would need a formal language for rules, which the authors judge most likely infeasible for early flexHEG versions. (protocol soundness) [13]
- Hardware-enabled guarantees (flexHEG) and guarantee processors: Governing all relevant chips depends on knowing where they are, through chip registries and detection of undeclared facilities. (coverage & hidden compute; waits on Chip registries and manufacturing records) [15]
- Remote detection of data centres: Wide-area, automated detection of data centres is not yet practical and needs large training datasets. (coverage & hidden compute) [21]
- Remote detection of data centres: No measured detection or false-alarm rates for finding undeclared facilities have been published. (adversarial validation) [19][21]
- Remote detection of data centres: Recent high-resolution imagery is costly, is limited by weather and needs trained analysts. (access & governance) [21]


## What the verifier sees

- Model weights: shown by none; depends on the design for none; hidden by Zero-knowledge proofs of inference and Hardware-enabled guarantees (flexHEG) and guarantee processors; not involved in Remote detection of data centres; unspecified for none.
- Inputs and outputs: shown by Zero-knowledge proofs of inference; depends on the design for none; hidden by Hardware-enabled guarantees (flexHEG) and guarantee processors; not involved in Remote detection of data centres; unspecified for none.
- Training data: shown by none; depends on the design for none; hidden by Hardware-enabled guarantees (flexHEG) and guarantee processors; not involved in Zero-knowledge proofs of inference and Remote detection of data centres; unspecified for none.

## Implementations

- Zero-knowledge proofs of inference: [Attestable zero-knowledge inference prover](https://trustbutveri.fyi/implementations/attestable-zk-inference/) (R1, product); [EZKL](https://trustbutveri.fyi/implementations/ezkl/) (R2, product); [Low-trust AI compute verification system overview](https://trustbutveri.fyi/implementations/low-trust-compute-verification-system-overview/) (R1, proposed architecture); [zkLLM](https://trustbutveri.fyi/implementations/zkllm/) (R2, research prototype)
- Hardware-enabled guarantees (flexHEG) and guarantee processors: none on the map
- Remote detection of data centres: none on the map

## Sources

1. zkLLM: Zero Knowledge Proofs for Large Language Models, H. Sun et al. (2024). https://doi.org/10.1145/3658644.3670334
2. zkllm-ccs2024: code for zkLLM: Zero Knowledge Proofs for Large Language Models, H. Sun (2024). https://github.com/jvhs0706/zkllm-ccs2024
3. ZKML: An Optimizing System for ML Inference in Zero-Knowledge Proofs, B.-J. Chen et al. (2024). https://doi.org/10.1145/3627703.3650088
4. NanoZK: Privacy-Preserving Verifiable Inference for Large Language Models via Layerwise Zero-Knowledge Proofs, Z. Wang (2026). https://arxiv.org/abs/2603.18046
5. Proving LLMs at Scale, Attestable (2026). https://attestable.com/blog/proving-llms-scale
6. Verifiable evaluations of machine learning models using zkSNARKs, T. South et al. (2024). https://arxiv.org/abs/2402.02675
7. Zkonduit EZKL Security Assessment, F. Casal et al. (2025). https://github.com/trailofbits/publications/blob/master/reviews/2025-03-zkonduit-ezkl-securityreview.pdf
8. DeepProve-1: The First zkML System to Prove a Full LLM Inference, Lagrange Labs (2025). https://lagrange.dev/blog/deepprove-1
9. Lagrange-Labs/deep-prove (GitHub repository), Lagrange Labs (2026). https://github.com/Lagrange-Labs/deep-prove
10. Hollow-LLM Attack: Computationally Trivial Weights in Zero-Knowledge Verification of LLM Inference, C. Gong et al. (2026). https://arxiv.org/abs/2607.28884
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. Pacing AI Requires Proof, Attestable (2026). https://attestable.com/blog/pacing-ai-requires-proof
13. Flexible Hardware-Enabled Guarantees for AI Compute, J. Petrie et al. (2025). https://arxiv.org/abs/2506.15093
14. Technical Options for Flexible Hardware-Enabled Guarantees, J. Petrie & O. Aarne (2025). https://arxiv.org/abs/2506.03409
15. International Security Applications of Flexible Hardware-Enabled Guarantees, O. Aarne & J. Petrie (2025). https://arxiv.org/abs/2506.15100
16. Hardware-Enabled Governance Mechanisms: Developing Technical Solutions to Exempt Items Otherwise Classified Under Export Control Classification Numbers 3A090 and 4A090, G. Kulp et al. (2024). https://www.rand.org/pubs/working_papers/WRA3056-1.html
17. 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
18. Hardware-Enabled Mechanisms for Verifying Responsible AI Development, A. O'Gara et al. (2025). https://arxiv.org/abs/2505.03742
19. Covert AI Projects, B. Halstead & T. Larsen (2026). https://ai-2040.com/supplements/covert-ai-projects
20. 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
21. Tracking Hyperscale AI Data Center Growth with Satellite Imagery, C. Krawec (2026). https://fas.org/publication/tracking-hyperscale/
22. Introducing the Frontier Data Centers Hub, Epoch AI (2025). https://epoch.ai/latest/introducing-the-frontier-data-centers-hub
23. AI Data Centers Documentation – Methodology, Epoch AI (2026). https://epoch.ai/data/data-centers-documentation/methodology