Proposal Explorer
ResetA verification proposal from the AI Verification Tech Map, from its records of 2026-10-08. https://trustbutveri.fyi/explorer/?mechanisms=M-0004,M-0013,M-0007
Analysis
Applied filters: none. Every filter is set to Any.
Overview
| Mechanism | Readiness | Open flaws |
|---|---|---|
| Zero-knowledge proofs of inference | R2 | 3 significant1 minor |
| Network taps and certifiers | R1 | 5 significant |
| Proofs of useful work for capacity accounting | R1 | 3 significant |
- Open flaws: n critical n significant n minor
Claim coverageNo claims yet
Add claims to see which ones the mechanisms address.
Properties3 built for an adversarial prover
- Built for an adversarial prover
- Zero-knowledge proofs of inference, Network taps and certifiers and Proofs of useful work for capacity accounting
- No new hardware needed
- Zero-knowledge proofs of inference and Proofs of useful work for capacity accounting
- Flaws since mitigated
- Verifier dictionary attacks on hashes in Network taps and certifiers 13
Attack testing3 mechanisms with published testing
Published attempts to break a system, including those that found failures. Testing history does not show that open flaws are resolved.
- Zero-knowledge proofs of inference: Independent red-team
- Network taps and certifiers: Analysis
- Proofs of useful work for capacity accounting: Analysis
Limits2 not yet demonstrated · 3 mechanisms with open significant findings
- Open significant flaws
11 flaws in 3 mechanisms
The proof covers a fixed-point approximation, not the floating-point model in Zero-knowledge proofs of inference
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 calls floating-point emulation in ZKPs an open problem. 1 5 7 11
Open question · Significant · Open. On the record
A proof speaks only for the computations that were proven in Zero-knowledge proofs of inference
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. 12
Theoretical argument · Significant · Open. On the record
Related mechanism R1 Proofs of useful work for capacity accounting: The record names proof-of-work accounting as the kind of compute accounting needed to show that proven inference was the only work done. A pointer, not evidence that this flaw is mitigated. In the proposal.
Proofs do not bind computational effort (Hollow-LLM) in Zero-knowledge proofs of inference
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. 10
Demonstrated attack · Significant · Open. On the record
Output nondeterminism leaves covert capacity in Network taps and certifiers
Hashing cannot remove information hidden in the outputs themselves. The Secure Gateway Device paper estimates that about 0.1 bit per token remains even with seed-synchronized replay checks. For a 200k-GPU inference cluster at full load (2,000 tokens per GPU per second), that is about 40 Mbit/s of covert egress, enough to move a 1 TB model in under three days. The paper names this the core remaining challenge and points to deterministic replay or active scrubbing of hardware-induced entropy. An independent study found that an adversary who chooses the prompts roughly doubles the bits leaked per token under Gumbel-based inference verification; see Bounding unexplained information in outputs. 13 20
Theoretical argument · Significant · Open. On the record
Related mechanism R3 Deterministic and bit-exact inference: Deterministic replay is one of the two remedies the flaw's source names. A pointer, not evidence that this flaw is mitigated. Add
Related mechanism R2 Bounding unexplained information in outputs: Bounds the hidden information outputs can carry by measuring what the declared computation fails to predict. A pointer, not evidence that this flaw is mitigated. Add
Some links cannot be passively tapped in Network taps and certifiers
Cankaya notes that copper-connected scale-up domains (for example NVL72 racks and TPU v7 cubes) are much harder to tap than fibre, and that optical budgets make passive taps impractical on 400GBASE-SR8 multimode links. Amodo found no taps advertised for 53 GBaud links as of May 2026. 14 21
Open question · Significant · Open. On the record
Encrypted fabrics hide plaintext from both parties in Network taps and certifiers
Cankaya notes that with TEE-protected sessions whose keys are ephemeral and managed inside the TEE, neither the operator nor the manufacturer can recover session keys after the session, so tapped traffic could not be opened for recomputation. For other encrypted fabrics, the operator can retain keys. 14
Open question · Significant · Open. On the record
Residual side channels in simple passive setups in Network taps and certifiers
Amodo's analysis of its own tapped prototype lists unvalidated header fields, timing of permitted traffic and variation in response formatting as residual channels, and concludes that the passive tap must be replaced by an active one. 16
Theoretical argument · Significant · Open. On the record
Completeness rests on physical monitoring left out of scope in Network taps and certifiers
The Secure Gateway Device paper assumes the facility is physically monitored, and states that the whole architecture depends on the device being the only communication channel. It names radio emanation, power-line signalling and thermal channels as covert channels beyond that scope. 13
Open question · Significant · Open. On the record
Related mechanism R1 Side-channel suppression for isolated facilities: Addresses the radio, power-line and thermal channels that network-level designs leave out. A pointer, not evidence that this flaw is mitigated. Add
Proves that work was done, not that no capacity remains in Proofs of useful work for capacity accounting
Proof-of-work accounting bounds unmonitored compute only relative to an estimate of what the actor has. Attestable states that the verifier "needs a credible estimate of the compute available" to the actor, and that a proof "cannot discover a datacenter that was never declared". 12
Theoretical argument · Significant · Open. On the record
Related mechanism R1 Chip registries and manufacturing records: A registry of chips is one basis for the estimate of available compute that the flaw's source says the verifier needs. A pointer, not evidence that this flaw is mitigated. Add
Related mechanism R1 Remote detection of data centres: Looks for data centres that were never declared, which a proof cannot discover. A pointer, not evidence that this flaw is mitigated. Add
Security rests on new hardness assumptions in Proofs of useful work for capacity accounting
Komargodski and Weinstein base security on hardness assumptions about batches of low-rank random linear equations, and list PoUW "from more standard or well-studied assumptions" as an open problem. Pearl's floating-point variant introduces a further "quantized-subspace hardness" assumption. 23 24
Open question · Significant · Open. On the record
Known shortcuts let a miner claim somewhat more work than it did in Proofs of useful work for capacity accounting
Pearl's specification lists known mining speedups: crafted inputs, precision shortcuts, seed grinding, work reuse, and faster kernels or hardware. A policy check caps the summands a miner may skip at one-sixteenth of those in a tile. For capacity bounding, any gap between work proven and work possible leaves spare capacity. 24
Theoretical argument · Significant · Open. On the record
- Open minor flaws
1 mechanism with minor findings
The model architecture is disclosed in Zero-knowledge proofs of inference
ZKML "requires that the model architecture (but not weights) is revealed", and zkLLM assumes a publicly known model structure. Architecture can be commercially sensitive. 1 3
Theoretical argument · Minor · Open. On the record
- Not yet demonstrated
- R1 Network taps and certifiers and R1 Proofs of useful work for capacity accounting
Possible additions5 for open flaws · 4 for dependencies
Mechanisms on the map that are not in the proposal. Pointers, not recommendations.
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- Bears on the open significant flaw “Output nondeterminism leaves covert capacity” in Network taps and certifiers. Deterministic replay is one of the two remedies the flaw's source names.
- Network taps and certifiers waits on it. Nondeterministic inference leaves covert capacity in outputs that hashing cannot remove.
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- Bears on the open significant flaw “Output nondeterminism leaves covert capacity” in Network taps and certifiers. Bounds the hidden information outputs can carry by measuring what the declared computation fails to predict.
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- Bears on the open significant flaw “Proves that work was done, not that no capacity remains” in Proofs of useful work for capacity accounting. A registry of chips is one basis for the estimate of available compute that the flaw's source says the verifier needs.
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- Bears on the open significant flaw “Proves that work was done, not that no capacity remains” in Proofs of useful work for capacity accounting. Looks for data centres that were never declared, which a proof cannot discover.
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- Bears on the open significant flaw “Completeness rests on physical monitoring left out of scope” in Network taps and certifiers. Addresses the radio, power-line and thermal channels that network-level designs leave out.
- Network taps and certifiers waits on it. Radio, power-line and thermal channels are not addressed by network-level designs.
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- Network taps and certifiers waits on it. Taps and gateway devices need tamper-evident housing and physical monitoring so that traffic cannot bypass them.
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- Network taps and certifiers depends on it.
Dependencies4 missing prerequisites · 12 blockers
- Missing prerequisites
- R3 Sampled inference recomputation needed by Network taps and certifiers Add
- R3 Deterministic and bit-exact inference needed by Network taps and certifiers Add
- R2 Tamper evidence for verifier devices needed by Network taps and certifiers Add
- R1 Side-channel suppression for isolated facilities needed by Network taps and certifiers Add
- Blockers
12 blockers recorded
- 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
- ZKML and zkLLM prove fixed-point arithmetic, and floating-point emulation in ZKPs is described as an open problem. Performance & compatibility 1 3 11
- 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
- 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
- Network taps and certifiers
- No complete verification tap has been demonstrated at production frontend link rates, and on the tested CPU no hash algorithm reached line rate with minimum-size frames. Performance & compatibility 21 26
- Nondeterministic inference leaves covert capacity in outputs that hashing cannot remove. Evidence binding. Waits on Deterministic and bit-exact inference 13
- Taps and gateway devices need tamper-evident housing and physical monitoring so that traffic cannot bypass them. Hardware trust. Waits on Tamper evidence for verifier devices 11 13
- Radio, power-line and thermal channels are not addressed by network-level designs. Coverage & hidden compute. Waits on Side-channel suppression for isolated facilities 13
- Red-teaming by specialists is called for but has not been reported. Adversarial validation 13
- Proofs of useful work for capacity accounting
- Bounding spare capacity needs a credible estimate of the compute available to the actor, including third-party access. Capacity bounds 12
- Proofs of work cannot find facilities that were never declared. Coverage & hidden compute 12
- As of September 2026 no implementation, demonstration or independent evaluation of proofs of work for capacity bounding has been published. Adversarial validation
- Zero-knowledge proofs of inference
What the verifier seesinputs and outputs shown by 1 · 2 depend on design
From the family or selected implementation's record.
- Model weights
Depends on the design for Network taps and certifiers and Proofs of useful work for capacity accounting.
Hidden by Zero-knowledge proofs of inference.
- Inputs and outputs
Shown by Zero-knowledge proofs of inference.
Depends on the design for Network taps and certifiers and Proofs of useful work for capacity accounting.
- Training data
Depends on the design for Network taps and certifiers.
Not involved: Zero-knowledge proofs of inference and Proofs of useful work for capacity accounting.
Exposure notes
- Zero-knowledge proofs of inference: The weights stay committed and hidden; the verifier knows each input and output it checks.
- Network taps and certifiers: Only hashes leave the site; records picked for a challenge are opened for replay at a verification facility.
- Proofs of useful work for capacity accounting: Checking a sampled tile of a matrix multiplication reveals that tile, which may hold model or input data; the authors suggest a zero-knowledge proof when the matrices must stay private.
Implementations7 systems
- Zero-knowledge proofs of inference
- R1 Attestable zero-knowledge inference prover Product, Attestable
- R2 EZKL Product, Zkonduit
- R1 Low-trust AI compute verification system overview Proposed architecture, Machine Intelligence Research Institute
- R2 zkLLM Research prototype, University of Waterloo
- Network taps and certifiers
- R1 AI 2040 inference-only verification stack Proposed architecture, AI Futures Project
- R1 Low-trust AI compute verification system overview Proposed architecture, Machine Intelligence Research Institute
- R1 SASH confidential network logger Research prototype, Singapore AI Safety Hub (SASH)
- Proofs of useful work for capacity accounting
- R3 Pearl proof-of-useful-work blockchain Open-source project, Pearl Research Labs
Sources26 cited
- zkLLM: Zero Knowledge Proofs for Large Language Models, H. Sun et al. (2024). Original
- zkllm-ccs2024: code for zkLLM: Zero Knowledge Proofs for Large Language Models, H. Sun (2024). Original
- ZKML: An Optimizing System for ML Inference in Zero-Knowledge Proofs, B.-J. Chen et al. (2024). Original
- NanoZK: Privacy-Preserving Verifiable Inference for Large Language Models via Layerwise Zero-Knowledge Proofs, Z. Wang (2026). Original
- Proving LLMs at Scale, Attestable (2026). Original
- Verifiable evaluations of machine learning models using zkSNARKs, T. South et al. (2024). Original
- Zkonduit EZKL Security Assessment, F. Casal et al. (2025). Original
- DeepProve-1: The First zkML System to Prove a Full LLM Inference, Lagrange Labs (2025). Original
- Lagrange-Labs/deep-prove (GitHub repository), Lagrange Labs (2026). Original
- Hollow-LLM Attack: Computationally Trivial Weights in Zero-Knowledge Verification of LLM Inference, C. Gong et al. (2026). Original
- A System Overview for Near-Term, Low-Trust AI Compute Verification, N. Cankaya (2026). Original
- Pacing AI Requires Proof, Attestable (2026). Original
- Fingerprinting All AI Cluster I/O Without Mutually Trusted Processors, N. Cankaya et al. (2026). Original
- The Fundamentals and Feasibility of Secure Network Taps for Verifying AI Datacenter Use, N. Cankaya (2026). Original
- Verification Plan, R. Dean (2026). Original
- Fitting a Network TAP to our Inference Verification Prototype, Amodo Design (2026). Original
- Amodo-Design/Inference-Recomputation-Prototype (GitHub repository), Amodo Design (2026). Original
- inference-verification: Inference Verification Prototype, Singapore AI Safety Hub (SASH) (2026). Original
- Internationalising AI Verification, Singapore AI Safety Hub (SASH) (2026). Original
- Adversarial Entropy Inflation Against Gumbel-Based Inference Verification, N. Kezins (2026). Original
- Network Tapping for AI Verification: A Technical Assessment, Amodo Design (2026). Original
- Mechanisms to Verify International Agreements About AI Development, A. Scher & L. Thiergart (2025). Original
- Proofs of Useful Work from Arbitrary Matrix Multiplication, I. Komargodski & O. Weinstein (2025). Original
- Pearl Floating Point Scheme Specification, Pearl Research Team (2026). Original
- pearl: Monorepo for the Pearl network, Pearl Research Labs (2026). Original
- Network Traffic Hashing, Amodo Design (2026). Original
Share the link to this proposal. This proposal is also available as plain text and JSON.
Filter mechanisms
Filters apply to mechanisms only. They describe the setting a proposal is for, and all are off by default. A mechanism that a filter rules out is flagged and does not count towards claim coverage. Selected implementations use their own record fields. A match means not excluded; conditional or unspecified exposure stays with a note. Passing a filter does not establish that the assumptions hold in a deployment.
All 25 mechanisms match.
Prover
How far can the party being checked be trusted?
The prover is the party being checked. Semi-trusted designs rely on part of its stack: usually the chip vendor's hardware root of trust, its firmware or counters, or its supply-chain records. Adversarial designs aim to hold even if it cheats wherever the checks allow, within their stated assumptions.
Keeps mechanisms whose threat model holds against at least this prover. Adversarial is the strongest assumption. Definitions
Verifier devices on site
May the verifier install its own hardware at the prover's sites?
Some mechanisms need a device the verifier owns or trusts at the prover's facility, such as a network tap, a bandwidth limiter or a sealed sensor. Choose Not allowed when the setting rules that out. Inspectors are not covered.
"Not allowed" removes mechanisms that need a retrofit device, such as a network tap or a sealed sensor. Definitions
Prover cooperation
How much must the prover take part?
Required: the prover takes part, for example by logging requests, producing proofs or opening records. Partial: some access, such as installing a device. Not required: works from outside, such as satellite imagery.
"Partial at most" removes mechanisms that need the prover's active participation. "Not required" keeps only those that work without it. Definitions
Chips
May the proposal depend on new 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.
"Existing chips only" removes mechanisms that need changes to future chip designs. Definitions
Minimum readiness
How mature must each mechanism be?
- Any (selected)25 match
- R1 Proposed25 match
- R2 Demonstrated15 match
- R3 In production4 match
- R4 Deployment-ready0 match
A level describes the public evidence for a mechanism's stated use, not its cost or feasibility. R3 can still have open critical flaws.
Keeps mechanisms whose readiness level is at least this one. Definitions
Attack testing
How hard has each mechanism been attacked in public?
The strongest published attempt to break the mechanism for its verification use: a security analysis, red-teaming by its developers or collaborators, or a red team independent of them.
Keeps mechanisms whose strongest published attack testing is at least this. Definitions
Keep hidden from the verifier
What must the verifier never see? Choose any.
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.
Removes mechanisms that show the asset to the verifier. Conditional or unspecified exposure stays with a note and needs checking against the privacy requirement.
Claims
A claim is something one party wants to verify about another party's AI hardware or software. Each claim's number shows how the proposal addresses it.
- Addressed. A mechanism in the proposal is aimed at this claim and is not excluded by the filters.
- Partly addressed. Only supporting mechanisms, or mechanisms aimed at it that the filters exclude.
- Unaddressed. No mechanism in the proposal addresses this claim.
Addressed means a mechanism in the proposal is aimed at the claim and is not excluded by your filters. It does not mean the claim is verified: check its assessed use, readiness, assumptions and open flaws.
Mechanisms
A mechanism is a general technique for verifying claims. Its badge is its readiness level for its stated use. An optional implementation choice narrows its assumptions, assessed use and claim links to that record. Lines join it to the claims it addresses. Click a line for details.Under its name it lists the claims it is aimed at or supports.
- Aimed at the claim: verifying it is a direct purpose of the mechanism.
- Supports the claim: helps verify it without being aimed at it.
- Faint: excluded by your filters, so it does not count towards claim coverage.
Overview
One row per mechanism in the proposal. Every mark comes from that mechanism's record, as listed in the panels below; what the verifier sees is the editors' reading of the record's text. Flaw counts are per mechanism. Summary counts name mechanisms with open findings, not a sum of attacks. Choosing an implementation narrows each row to that record's assessed use; family findings remain as context.
What the verifier sees
For model weights, inputs and outputs, and training data. This is the editors' reading of each mechanism's record (its threat model, how it works and its limitations), not a field of the record. Shown: the verifier sees it. Depends: on the design or variant, or the verifier sees only samples. Hidden: the verifier sees only commitments, hashes, proofs or results. Not involved: the record does not handle it. Unspecified: the selected implementation has no asset-specific assessment here.
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. They are pointers, not recommendations: each brings its own readiness level and flaws, and none is claimed to close a flaw. Links from flaws are the editors' reading of the two records.
Start from a goal
A goal is something a rule or agreement about AI sets out to achieve. Choosing one loads the claims it needs verified. Your mechanisms and filters stay as they are.
- Cap frontier training2 direct, 3 supportingKeep every AI training run below an agreed amount of compute.
- Pause frontier AI development2 direct, 3 supportingStop new AI training runs and experiments for an agreed period, while existing models stay in service.
- Deploy only evaluated models2 direct, 3 supportingDeploy powerful AI models widely only after their risks have been evaluated and judged manageable.
- Prevent catastrophic misuse2 direct, 2 supportingKeep capable AI models from helping anyone carry out catastrophic attacks, such as biological or chemical ones.
- Prevent weight theft1 direct, 1 supportingKeep the weights of capable AI models from being copied out of the facilities that hold them.
- Enforce chip export controls1 directKeep export-controlled AI chips at the destinations they were authorised for.
Start from a published design
Choosing a design loads the mechanisms its record realises or depends on. If the proposal has no claims yet, it also loads the claims that record says the design addresses.
- AI 2040 inference-only verification stack7 mechanismsProposed architecture, AI Futures Project
- Low-trust AI compute verification system overview7 mechanismsProposed architecture, Machine Intelligence Research Institute
- RAND secure inference data center (SIDC) design3 mechanismsProposed architecture, RAND