# 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-0019&implementations=M-0004:I-0003 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. None set. Every mechanism on the map was available. ## 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 / zkLLM | Research demonstration | Published security analysis | Adversarial | Analysis | None | 0 / 0 / 0 | hidden | shown | not involved | | Chip registries and manufacturing records | Proposed | Published security analysis | Semi-trusted | Analysis | Existing features | 0 / 1 / 0 | not involved | not involved | not involved | ## Claims No claims chosen. ## Mechanisms ### Zero-knowledge proofs of inference zkLLM is a GPU-accelerated zero-knowledge proof system that proves a large language model's output came from committed weights without revealing those weights. ([Zero-knowledge proofs of inference](https://trustbutveri.fyi/mechanisms/zk-proofs-of-inference/)) - Assessment: selected implementation [zkLLM](https://trustbutveri.fyi/implementations/zkllm/). - Development: Research demonstration (legacy code R2), assessed for proving an output came from committed weights, against a prover who cheats. - 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: required. Attack testing: analysis. Category: Cryptographic & computational. - What the verifier sees: model weights hidden; inputs and outputs shown; training data not involved. zkLLM's stated setting keeps model parameters private while the verifier supplies the prompt and checks the returned output. ### 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. ## Properties **Built for an adversarial prover** - Zero-knowledge proofs of inference **No new hardware needed** - Zero-knowledge proofs of inference - Chip registries and manufacturing records ## 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 / zkLLM: Analysis - Chip registries and manufacturing records: 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/) [10]. 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. **Family finding context** - Context for zkLLM. Findings from the mechanism family appear here as context. They apply to an implementation only when its own record lists them, under the conditions stated there. 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][3][4][5]. 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. - Context for zkLLM. Findings from the mechanism family appear here as context. They apply to an implementation only when its own record lists them, under the conditions stated there. 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/) [6]. 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. - Context for zkLLM. Findings from the mechanism family appear here as context. They apply to an implementation only when its own record lists them, under the conditions stated there. 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][7]. ZKML "requires that the model architecture (but not weights) is revealed", and zkLLM assumes a publicly known model structure. Architecture can be commercially sensitive. - Context for zkLLM. Findings from the mechanism family appear here as context. They apply to an implementation only when its own record lists them, under the conditions stated there. 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/) [8]. 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. **Scope limitations** - Reference code is interactive and runs prover and verifier together (scope limitation, open question, in zkLLM; https://trustbutveri.fyi/implementations/zkllm/evidence/flaws/1/) [2]. The README states that prover and verifier work "are implemented side-by-side", and that intermediate values written to files are for the prover's reference only. It says an industrial deployment would need to separate the two and apply Fiat–Shamir to make proofs non-interactive. The released code gives a verifier no standalone check. - Proves a fixed-point approximation of a publicly known architecture (scope limitation, theoretical argument, in zkLLM; https://trustbutveri.fyi/implementations/zkllm/evidence/flaws/2/) [1]. The prover's model must have a "publicly known structure". Tensors are discretised by scaling and rounding. The authors report perplexity changes of 0.008 to 0.09 on C4. The proof covers the quantised computation. - 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/) [11][13]. 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, not 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/) [11]. 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** - Chip registries and manufacturing records: Proposed (legacy code R1), 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 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** - Zero-knowledge proofs of inference: Proving takes about 13 minutes (803 seconds) of A100 time per 2,048-token forward pass at 13B parameters, plus a one-time weight commitment of 16 to 21 minutes. (performance & compatibility) [1] - Zero-knowledge proofs of inference: The repository was archived on 10 July 2025 and the author states there is no plan for upgrades or maintenance. (performance & compatibility) [2] - Zero-knowledge proofs of inference: No security audit of the code has been carried out. (adversarial validation) [2] - 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) [9][10] - Chip registries and manufacturing records: Linking records to physical chips needs hard-to-spoof unique IDs and inspections. (hardware trust) [9][10][11] - Chip registries and manufacturing records: Chips produced before a registry starts must be reconstructed from supplier records. (coverage & hidden compute) [11][13] ## What the verifier sees - Model weights: shown by none; depends on the design for none; hidden by Zero-knowledge proofs of inference; not involved in Chip registries and manufacturing records; unspecified for none. - Inputs and outputs: shown by Zero-knowledge proofs of inference; depends on the design for none; hidden by none; not involved in 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 Zero-knowledge proofs of inference and Chip registries and manufacturing records; 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) - Chip registries and manufacturing records: 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. Proving LLMs at Scale, Attestable (2026). https://attestable.com/blog/proving-llms-scale 4. Zkonduit EZKL Security Assessment, F. Casal et al. (2025). https://github.com/trailofbits/publications/blob/master/reviews/2025-03-zkonduit-ezkl-securityreview.pdf 5. 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 6. Pacing AI Requires Proof, Attestable (2026). https://attestable.com/blog/pacing-ai-requires-proof 7. ZKML: An Optimizing System for ML Inference in Zero-Knowledge Proofs, B.-J. Chen et al. (2024). https://doi.org/10.1145/3627703.3650088 8. Hollow-LLM Attack: Computationally Trivial Weights in Zero-Knowledge Verification of LLM Inference, C. Gong et al. (2026). https://arxiv.org/abs/2607.28884 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. 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 11. 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 12. Hardware-Level Governance of AI Compute: A Feasibility Taxonomy for Regulatory Compliance and Treaty Verification, S. Ansari (2026). https://arxiv.org/abs/2604.04712 13. Covert AI Projects, B. Halstead & T. Larsen (2026). https://ai-2040.com/supplements/covert-ai-projects