Proposal Explorer
ResetA verification proposal from the AI Verification Tech Map, from its records of 2026-10-08. https://trustbutveri.fyi/explorer/?mechanisms=M-0024,M-0013&prover=cooperative&cols=sees
Analysis
Applied filters: Prover: Cooperative. Not set (Any): Verifier devices on site, Prover cooperation, Chips, Minimum readiness, Attack testing, Keep hidden from the verifier.
Overview
| Mechanism | Readiness | Open flaws | Verifier sees |
|---|---|---|---|
| Bounding unexplained information in outputs | R2 | 4 significant | W dependsI dependsT not involved |
| Network taps and certifiers | R1 | 5 significant | W dependsI dependsT depends |
- Open flaws: n critical n significant n minor
- Verifier sees model weights (W), inputs and outputs (I), training data (T): W shown W depends W hidden W not involved W unspecified
Claim coverageNo claims yet
Add claims to see which ones the mechanisms address.
Properties2 built for an adversarial prover
- Built for an adversarial prover
- Bounding unexplained information in outputs and Network taps and certifiers
- Flaws since mitigated
- Verifier dictionary attacks on hashes in Network taps and certifiers 6
Attack testing2 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.
- Bounding unexplained information in outputs: Independent red-team
- Network taps and certifiers: Analysis
Limits1 not yet demonstrated · 2 mechanisms with open significant findings
- Open significant flaws
9 flaws in 2 mechanisms
Prompt-controlled entropy inflation widens the covert channel in Bounding unexplained information in outputs
Gumbel-based inference verification tolerates token choices that honest GPU nondeterminism could produce, and the size of that tolerated set grows with the model's output entropy. Kezins, an independent researcher, showed that an adversary who controls the prompt distribution can raise output entropy and roughly double the bits leaked per token. Across six models of 1 to 32 billion parameters, this cut the slowdown from 146–254 times under benign prompts to 60–118 times. Kezins argues that architectures built on the same unexplained-information bound inherit this attack surface, and recommends calibrating tolerances against local token entropy rather than benign traffic. 2 3
Demonstrated attack · Significant · Open. On the record
Related mechanism R3 Deterministic and bit-exact inference: Bit-exact replay would remove the tolerance for numerical noise that sets the size of this channel. The record notes that it needs full hardware and software metadata. A pointer, not evidence that this flaw is mitigated. Add
Information the declared computation explains is not bounded in Bounding unexplained information in outputs
The bound limits unexplained bits only. Outputs that the declared computation fully explains can still carry valuable information: a compression study notes that an adversary with inference access can extract more proprietary information per bit than naive transmission allows. 1 4
Theoretical argument · Significant · Open. On the record
Channels other than checked outputs are outside the bound in Bounding unexplained information in outputs
The inference-verification scheme treats side channels as out of scope. A low-trust system design argues that suppressing physical covert bandwidth below kilobits per second is much more achievable than aiming for zero, and that a malicious device can leak one bit of information by deliberately outputting a wrong result. 2 5
Theoretical argument · Significant · Open. On the record
Related mechanism R1 Side-channel suppression for isolated facilities: Physical side channels need separate suppression, which is this mechanism's purpose. A pointer, not evidence that this flaw is mitigated. Add
The facility-level design is untested in Bounding unexplained information in outputs
The compute-verification architecture is described with protocol details, potential attacks and prototyping plans, but no prototype results have been published. 1
Open question · 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. 3 6
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. In the proposal.
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. 7 13
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. 7
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. 9
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. 6
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
- Not yet demonstrated
- R1 Network taps and certifiers
Possible additions2 for open flaws · 5 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 “Prompt-controlled entropy inflation widens the covert channel” in Bounding unexplained information in outputs. Bit-exact replay would remove the tolerance for numerical noise that sets the size of this channel. The record notes that it needs full hardware and software metadata.
- 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.
- Bounding unexplained information in outputs waits on it. Tolerance for numerical nondeterminism sets the size of the residual channel; bit-exact replay would remove it but needs full hardware and software metadata.
- 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 “Channels other than checked outputs are outside the bound” in Bounding unexplained information in outputs. Physical side channels need separate suppression, which is this mechanism's purpose.
- 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.
- Bounding unexplained information in outputs waits on it. Physical side channels need separate suppression, and one design treats a low residual bandwidth, rather than zero, as the realistic target.
- Network taps and certifiers waits on it. Radio, power-line and thermal channels are not addressed by network-level designs.
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- Bounding unexplained information in outputs waits on it. The prover's compute must be isolated so that all traffic passes through the verifier's interlock; any unmonitored path voids the bound.
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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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- Bounding unexplained information in outputs depends on it.
- Network taps and certifiers depends on it.
Dependencies5 missing prerequisites · 3 shared foundations · 10 blockers
- Missing prerequisites
- R3 Sampled inference recomputation needed by Bounding unexplained information in outputs and Network taps and certifiers Add
- R2 Bandwidth limits and compartmentalization needed by Bounding unexplained information in outputs Add
- R1 Side-channel suppression for isolated facilities needed by Bounding unexplained information in outputs and 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
- Shared foundations
- Sampled inference recomputation relied on by Bounding unexplained information in outputs and Network taps and certifiers
- Side-channel suppression for isolated facilities relied on by Bounding unexplained information in outputs and Network taps and certifiers
- Deterministic and bit-exact inference relied on by Bounding unexplained information in outputs and Network taps and certifiers
- Blockers
10 blockers recorded
- Bounding unexplained information in outputs
- The prover's compute must be isolated so that all traffic passes through the verifier's interlock; any unmonitored path voids the bound. Coverage & hidden compute. Waits on Bandwidth limits and compartmentalization 1
- Physical side channels need separate suppression, and one design treats a low residual bandwidth, rather than zero, as the realistic target. Coverage & hidden compute. Waits on Side-channel suppression for isolated facilities 2 5
- Tolerance for numerical nondeterminism sets the size of the residual channel; bit-exact replay would remove it but needs full hardware and software metadata. Protocol soundness. Waits on Deterministic and bit-exact inference 3 5
- Recomputation over confidential weights and inputs needs a protected setting: prover recomputation in a verifier-controlled enclosure, verifier recomputation in a prover-controlled enclosure, or zero-knowledge proofs. Privacy & leakage 1
- No prototype of the facility-level architecture exists to red-team. Adversarial validation 1
- 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 13 14
- Nondeterministic inference leaves covert capacity in outputs that hashing cannot remove. Evidence binding. Waits on Deterministic and bit-exact inference 6
- 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 5 6
- Radio, power-line and thermal channels are not addressed by network-level designs. Coverage & hidden compute. Waits on Side-channel suppression for isolated facilities 6
- Red-teaming by specialists is called for but has not been reported. Adversarial validation 6
- Bounding unexplained information in outputs
What the verifier sees2 depend on design
From the family or selected implementation's record.
- Model weights
Depends on the design for Bounding unexplained information in outputs and Network taps and certifiers.
- Inputs and outputs
Depends on the design for Bounding unexplained information in outputs and Network taps and certifiers.
- Training data
Depends on the design for Network taps and certifiers.
Not involved: Bounding unexplained information in outputs.
Exposure notes
- Bounding unexplained information in outputs: Depends on where recomputation runs: in a sealed enclosure, or with zero-knowledge proofs, the verifier need not see the weights or the traffic.
- Network taps and certifiers: Only hashes leave the site; records picked for a challenge are opened for replay at a verification facility.
Implementations3 systems
- Bounding unexplained information in outputs
- None on the map
- 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)
Sources14 cited
- Verifying AI Compute by Bounding Unexplained Information Exfiltration, J. Petrie & Y. Mühlhäuser (2026). Original
- Verifying LLM Inference to Detect Model Weight Exfiltration, R. Rinberg et al. (2025). Original
- Adversarial Entropy Inflation Against Gumbel-Based Inference Verification, N. Kezins (2026). Original
- Haiku to Opus in Just 10 bits: LLMs Unlock Large Compression Gains, R. Rinberg et al. (2026). Original
- A System Overview for Near-Term, Low-Trust AI Compute Verification, N. Cankaya (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
- Network Tapping for AI Verification: A Technical Assessment, Amodo Design (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.
25 of 25 mechanisms match · Clear all
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