Proposal Explorer
ResetA verification proposal from the AI Verification Tech Map, from its records of 2026-10-08. https://trustbutveri.fyi/explorer/?mechanisms=M-0018,M-0013&implementations=M-0013:I-0012&coop=partial
Analysis
Applied filters: Prover cooperation: Partial at most. Not set (Any): Prover, Verifier devices on site, Chips, Minimum readiness, Attack testing, Keep hidden from the verifier.
Overview
| Mechanism | Readiness | Open flaws |
|---|---|---|
| Chip location verification ⚠ excluded by your filters: prover cooperation required | R1 | 4 significant |
| Network taps and certifiersLow-trust AI compute verification system overview ⚠ excluded by your filters: prover cooperation required | R1 | 2 significant1 minorFamily context below |
- Open flaws: n critical n significant n minor
- ⚠ Dimmed: excluded by your filters
Claim coverageNo claims yet
Add claims to see which ones the mechanisms address.
PropertiesNone recorded
- Not counted
- Excluded by your filters: Chip location verification and Network taps and certifiers
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.
- Chip location verification: Analysis · excluded by filters
- Network taps and certifiers, Low-trust AI compute verification system overview: Analysis · excluded by filters
Limits1 family with findings to check · 2 excluded by filters · 2 not yet demonstrated · 2 mechanisms with open significant findings
- Excluded by your filters
- Chip location verification Prover cooperation is required; the filter allows partial at most.
- Network taps and certifiers Prover cooperation is required; the filter allows partial at most.
- Family findings
- Network taps and certifiers
Context for Low-trust AI compute verification system overview. These findings concern the family or other implementations; applicability must be checked against their stated scope.
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. 9 10
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. 11 12
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. 11
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. 13
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. 9
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
Verifier dictionary attacks on hashes in Network taps and certifiers
Hashes of very short outputs could be brute-forced by the verifier. The paper recommends hashing at least 5 tokens together, or at least 10 if the attacker filters for likely tokens. 9
Theoretical argument · Minor · Mitigated. On the record
- Network taps and certifiers
- Open significant flaws
6 flaws in 2 mechanisms
Extracting a chip's key lets another device answer for it in Chip location verification
Ping-based protocols rely on cryptographic keys stored on the chip. Tee and Happel argue that an adversary with physical access could extract these keys and so compromise location verification. They propose GPU fingerprints as a mitigation, so far tested on 24 GPUs. Brass and Aarne assume the keys are stored securely, for example in a TPM. 1 6
Theoretical argument · Significant · Open. On the record
Added delay can shift an estimated position in Chip location verification
Brass and Aarne cite internet-geolocation research in which artificially increased round-trip times moved the estimated location by up to 1,000 km, with a 74% chance of avoiding detection. Avellar and Grunewald list inflated ping times from circuitous routing as an evasion route. Added delay only loosens a distance bound, and Brass and Aarne propose a hard time limit as the counter: a chip that replies too slowly cannot be ruled out of a restricted location. 1 4
Demonstrated attack · Significant · Open. On the record
Faster-than-assumed network paths in Chip location verification
Brass and Aarne list dark fibre and other private high-speed interconnects as ways to lower measured delays artificially. They judge that leasing dark fibre would probably not be a considerable challenge for covertly or openly adversarial actors. Avellar and Grunewald note that this can make a chip appear to be somewhere else entirely. A limit set at the vacuum speed of light cannot be beaten, but it makes honest chips fail more often. 1 4
Theoretical argument · Significant · Open. On the record
Compromised landmarks can falsify measurements in Chip location verification
A party that controls landmark servers can report false timing. Brass and Aarne cite research in which manipulating a third of the landmarks shifted the estimated location by about 700 km. Avellar and Grunewald note that compromised landmarks let adversaries spoof travel-time measurements directly. The draft specification asks verifiers to require anchors in diverse places, run by several independent operators. 1 4 5
Theoretical argument · Significant · Open. On the record
Mismatches cannot be attributed to cheating or error in Low-trust AI compute verification system overview
A failed hash or replay does not show whether it came from an evasion attempt, a random bit flip or an evaluation error. The author notes that if detected anomalies can plausibly be waved off as malfunctions, deterrence becomes less effective, so the parties need an agreed escalation procedure that ends in attribution. 8
Theoretical argument · Significant · Open. On the record
Inspector agents may be manipulable in Low-trust AI compute verification system overview
Automated compliance screening with LLM-based inspector agents must resist prompt-injection attacks. Adversarially trained systems might hide malicious actions with steganography, which makes backdoor detection an open problem. 8
Open question · Significant · Open. On the record
- Open minor flaws
1 mechanism with minor findings
Deliberate faults leak a bit each in Low-trust AI compute verification system overview
A malicious device can leak one bit by deliberately outputting a wrong result, which blocks a disclosure when the cross-comparison fails. The design therefore needs a pre-agreed budget of tolerated faults. 8
Theoretical argument · Minor · Open. On the record
- Not yet demonstrated
- R1 Chip location verification and R1 Network taps and certifiers
Possible additions5 for dependencies
Mechanisms on the map that are not in the proposal. Pointers, not recommendations.
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- Network taps and certifiers waits on it. Tamper-evident, rapidly mass-manufacturable and retrofittable enclosures for side-channel defence are an open research question, and physical security against covert communication in every monitored data centre is challenging.
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- Network taps and certifiers waits on it. A mass-manufacturable, good-enough side-channel defence, particularly power-line filtering, has not been constructed or red-teamed.
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Excluded by filters: prover cooperation required
- Network taps and certifiers waits on it. Exact replay needs complete hardware and software metadata, and the tolerable slowdown from emulation is an open question.
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Excluded by filters: prover cooperation required
- Network taps and certifiers waits on it. Distinguishing one server's DRAM contents from another's by challenge-response timing, and a general challenge-response protocol for diverse data types, are open.
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Excluded by filters: prover cooperation required
- Chip location verification depends on it.
Dependencies2 missing prerequisites · 10 blockers
- Missing prerequisites
- R3 TEE remote attestation for AI workloads needed by Chip location verification Add
- R3 Deterministic and bit-exact inference needed by Network taps and certifiers Add
- Blockers
10 blockers recorded
- Chip location verification
- No public code or reproducible end-to-end location results are available for the reported H100 prototype. Adversarial validation 2 3
- Per-chip keys must be provisioned and protected against extraction; hardware-integrated, tamper-resistant versions still need R&D. Hardware trust 1 6 14
- The time limit forces a trade-off: a limit at the speed of light in fibre can be beaten by faster links, while one at the vacuum speed of light makes honest chips fail often. Protocol soundness 1
- A trusted landmark network must be built and secured, and who should operate it, under what oversight, is unsettled. Access & governance 1 4
- Network taps and certifiers
- Empirical feasibility of passive optical splitting at 53–112 GBaud under realistic conditions is an open question. Performance & compatibility. Waits on Network taps and certifiers 8
- Exact replay needs complete hardware and software metadata, and the tolerable slowdown from emulation is an open question. Performance & compatibility. Waits on Deterministic and bit-exact inference 8
- Tamper-evident, rapidly mass-manufacturable and retrofittable enclosures for side-channel defence are an open research question, and physical security against covert communication in every monitored data centre is challenging. Hardware trust. Waits on Tamper evidence for verifier devices 8
- A mass-manufacturable, good-enough side-channel defence, particularly power-line filtering, has not been constructed or red-teamed. Coverage & hidden compute. Waits on Side-channel suppression for isolated facilities 8
- Distinguishing one server's DRAM contents from another's by challenge-response timing, and a general challenge-response protocol for diverse data types, are open. Coverage & hidden compute. Waits on Timed challenge-response and memory-occupation challenges 8
- The threat model is under-developed and needs input from cybersecurity and AI threat-modelling experts. Adversarial validation 8
- Chip location verification
What the verifier sees1 unspecified
From the family or selected implementation's record.
- Model weights
Not involved: Chip location verification.
Unspecified for Network taps and certifiers. Check the implementation record.
- Inputs and outputs
Not involved: Chip location verification.
Unspecified for Network taps and certifiers. Check the implementation record.
- Training data
Not involved: Chip location verification.
Unspecified for Network taps and certifiers. Check the implementation record.
Exposure notes
- Chip location verification: Times signed replies from chips; it does not handle model data.
- Network taps and certifiers, Low-trust AI compute verification system overview: This Explorer has no asset-specific exposure assessment for this implementation. Check its source and deployment assumptions.
Implementations4 systems
- Chip location verification
- R1 Lucid sovereignty (location) certificates Standard, Lucid Computing
- 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
- Location Verification for AI Chips, A. Brass & O. Aarne (2024). Original
- Location Verification for AI Chips (issue brief), A. Brass (2025). Original
- Ping-based Location, Ulyssean (2025). Original
- Near-Term Verification Methods for AI Chip Exports, B. Avellar & E. Grunewald (2026). Original
- Sovereignty Certificates: draft specification, version 0.1.0, Sovereignty Certificates Working Group (2025). Original
- GPU Fingerprinting for Location Verification, W. Tee & J. Happel (2026). Original
- Secure, Governable Chips: Using On-Chip Mechanisms to Manage National Security Risks from AI & Advanced Computing, O. Aarne et al. (2024). 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
- Adversarial Entropy Inflation Against Gumbel-Based Inference Verification, N. Kezins (2026). Original
- The Fundamentals and Feasibility of Secure Network Taps for Verifying AI Datacenter Use, N. Cankaya (2026). Original
- Network Tapping for AI Verification: A Technical Assessment, Amodo Design (2026). Original
- Fitting a Network TAP to our Inference Verification Prototype, Amodo Design (2026). Original
- Hardware-Level Governance of AI Compute: A Feasibility Taxonomy for Regulatory Compliance and Treaty Verification, S. Ansari (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.
6 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)6 match
- R1 Proposed6 match
- R2 Demonstrated3 match
- R3 In production0 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