Proposal Explorer
ResetA verification proposal from the AI Verification Tech Map, from its records of 2026-10-09. https://trustbutveri.fyi/explorer/?mechanisms=M-0014,M-0009,M-0010,M-0020
Analysis
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Overview
| Mechanism | Development | Security evidence | Open failures |
|---|---|---|---|
| Bandwidth limits and compartmentalization | Research demo | Published security analysis | 2 significant |
| Hardware-enabled guarantees (flexHEG) and guarantee processors | Proposed | Published security analysis | 3 significant |
| On-chip telemetry from timing, memory and performance counters | Research demo | Published attack testing | 2 significant |
| Remote detection of data centres | Proposed | Published security analysis | none |
- Open failures: 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
- Bandwidth limits and compartmentalization, Hardware-enabled guarantees (flexHEG) and guarantee processors and Remote detection of data centres
- No new hardware needed
- On-chip telemetry from timing, memory and performance counters and Remote detection of data centres
Attack testing4 mechanisms with published 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.
Limits8 scope limitations · 4 open questions · 2 not yet demonstrated · 3 mechanisms with open significant failures
- Open significant failures
7 failures in 3 mechanisms
Operator control of pod routing collapses the bound in Bandwidth limits and compartmentalization
Lucid's analysis finds that if the operator can freely assign pods to routers, it could dedicate a whole cell of 100 or more pods to one pipeline stage. The bound then falls to about 90–220x uncompressed and as low as about 25x with compression. The proposed mitigation, auditor-controlled random assignment that is periodically re-randomized, has not been implemented. 2
Known failure · Theoretical argument · Significant · Open. On the record
Parallel scale-up switches are hard enforcement points in Bandwidth limits and compartmentalization
In GB200 topologies, GPUs reach GPUs in other nodes through NVSwitches without a NIC on the path. Amodo notes that limits are hard to enforce there because many switches work in parallel, so compromising one or two would bypass the limit. 5
Known failure · Theoretical argument · Significant · Open. On the record
State attackers can likely defeat current secure enclosures in Hardware-enabled guarantees (flexHEG) and guarantee processors
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. 9 11
Known failure · Theoretical argument · Significant · Open. On the record
Firmware-only retrofits rely on Secure Boot, which fault injection can bypass in Hardware-enabled guarantees (flexHEG) and guarantee processors
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. 9
Known failure · Theoretical argument · Significant · Open. On the record
FLOP accounting can be laundered through external data in Hardware-enabled guarantees (flexHEG) and guarantee processors
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. 9
Known failure · Theoretical argument · Significant · Open. On the record
Adversarially disguised fine-tuning partly evades classification in On-chip telemetry from timing, memory and performance counters
Across 20 evasion strategy families in five monitor-evader rounds, the classifier's accuracy against the most challenging disguised workloads held out from training was 43–87%. White-box LoRA fine-tuning was the only evasion family detected less than half the time. The evaluation covered single nodes, PyTorch-level evasion and NVIDIA hardware. 16
Known failure · Demonstrated attack · Significant · Open. On the record
Counters leak information about protected workloads in On-chip telemetry from timing, memory and performance counters
Performance counters have been used as a side channel against TEEs, for example in CounterSEVeillance. NVIDIA disables performance counters in full confidential-computing mode, stating that they could provide an avenue for side-channel attacks. Richer counters for verification therefore pull against confidentiality. 17 18
Known failure · Theoretical argument · Significant · Open. On the record
- Scope limitations
Undeclared local storage raises per-pod capacity in Bandwidth limits and compartmentalization
More memory or storage per pod helps an adversary. Lucid requires per-pod storage to be declared, capped and physically inspected. 2
Scope limitation · Theoretical argument. On the record
Training within one pod is not covered in Bandwidth limits and compartmentalization
Lucid's bounds concern pre-training models larger than the pods are sized for. Training models that fit in one pod, fine-tuning and reinforcement-learning post-training within one pod are outside the modelled threat. 2
Scope limitation · Open question. On the record
Many important rules cannot be checked on-chip in Hardware-enabled guarantees (flexHEG) and guarantee processors
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. 8 10
Scope limitation · Theoretical argument. On the record
Coverage stops at flexHEG-equipped chips in Hardware-enabled guarantees (flexHEG) and guarantee processors
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". 8 10
Scope limitation · Open question. On the record
Related mechanism Proposed Chip registries and manufacturing records: Accounts for which chips exist and who holds them. A pointer, not evidence that this failure is mitigated. Add
Related mechanism Proposed Remote detection of data centres: Looks for undeclared facilities that hold other chips. A pointer, not evidence that this failure is mitigated. In the proposal.
Software-read telemetry can be forged by the operator in On-chip telemetry from timing, memory and performance counters
NVML-based classification assumes trustworthy telemetry. Without a tamper-resistant read path, an authenticated telemetry channel and secure boot of the monitoring software, an operator who controls the full software stack could forge counter values. Monfared et al. start from the same premise: current GPUs expose little trusted telemetry and can be modified or virtualized. 14 16
Scope limitation · Theoretical argument. On the record
Related mechanism Proposed Hardware-enabled guarantees (flexHEG) and guarantee processors: A guarantee processor on the chip would give the tamper-resistant, authenticated telemetry path the flaw says is missing. A pointer, not evidence that this failure is mitigated. In the proposal.
Timing challenges do not identify the individual chip in On-chip telemetry from timing, memory and performance counters
GEMM and VDF challenges can be answered by identical GPUs elsewhere, and floating-point fingerprints distinguish GPU models, not individual devices. GPU virtualization adds timing leakage that prevents attributing compute use. 14
Scope limitation · Theoretical argument. On the record
Facilities can be disguised or hidden in Remote detection of data centres
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. 19
Scope limitation · Theoretical argument. On the record
Small sites may not be detectable in Remote detection of data centres
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. 19 21
Scope limitation · Theoretical argument. On the record
Related mechanism Proposed Chip registries and manufacturing records: Accounts for chips from the fab onwards, which does not depend on a site being visible. A pointer, not evidence that this failure is mitigated. Add
- Open questions
Low-communication training reduces the bandwidth training needs in Bandwidth limits and compartmentalization
DiLoCo matched fully synchronous training on 8 workers while communicating 500 times less. Rahman writes that this family of methods theoretically allows large-scale training with less than 100 Mbps. Lucid includes these methods in its bounds, but notes that extreme activation compression, architectures with unusually small inter-layer widths, or modular paradigms could erode the margin. 2 6 7
Open question · Theoretical argument. On the record
Supply-chain diversion and hidden backdoors in Hardware-enabled guarantees (flexHEG) and guarantee processors
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. 9 10
Open question · Open question. On the record
Related mechanism Proposed Chip registries and manufacturing records: 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. A pointer, not evidence that this failure is mitigated. Add
No quantified error rates or formal thresholds for timing primitives in On-chip telemetry from timing, memory and performance counters
Monfared et al. state that false-positive and false-negative rates are not quantified and leave hardware-specific formal thresholds to future work. 14
Open question · Open question. On the record
Search for unknown sites is undemonstrated in Remote detection of data centres
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". 21
Open question · Open question. On the record
- Not yet demonstrated
- Proposed Hardware-enabled guarantees (flexHEG) and guarantee processors and Proposed Remote detection of data centres
- Need new chip designs
- Hardware-enabled guarantees (flexHEG) and guarantee processors
Possible additions4 for dependencies
Mechanisms on the map that are not in the proposal. Pointers, not recommendations.
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- Bandwidth limits and compartmentalization waits on it. Shaping devices and routing assignments must be trusted by both parties; Amodo has not yet fully analysed resilience to a compromised DPU.
- 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.
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- 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.
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- Bandwidth limits and compartmentalization waits on it. The verifier must know that all traffic leaving a pod crosses the capped, monitored links.
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- Hardware-enabled guarantees (flexHEG) and guarantee processors depends on it.
- On-chip telemetry from timing, memory and performance counters depends on it.
Dependencies3 missing prerequisites · 2 shared foundations · 16 blockers
- Missing prerequisites
- Research demo Tamper evidence for verifier devices needed by Bandwidth limits and compartmentalization Add
- Operational use TEE remote attestation for AI workloads needed by Hardware-enabled guarantees (flexHEG) and guarantee processors and On-chip telemetry from timing, memory and performance counters Add
- Proposed Chip registries and manufacturing records needed by Hardware-enabled guarantees (flexHEG) and guarantee processors Add
- Shared foundations
- Tamper evidence for verifier devices relied on by Bandwidth limits and compartmentalization and Hardware-enabled guarantees (flexHEG) and guarantee processors
- TEE remote attestation for AI workloads relied on by Hardware-enabled guarantees (flexHEG) and guarantee processors and On-chip telemetry from timing, memory and performance counters
- Blockers
16 blockers recorded
- Bandwidth limits and compartmentalization
- No cap that a verifier can check has been implemented or red-teamed. Adversarial validation 2
- The verifier must know that all traffic leaving a pod crosses the capped, monitored links. Coverage & hidden compute. Waits on Network taps and certifiers 3
- Shaping devices and routing assignments must be trusted by both parties; Amodo has not yet fully analysed resilience to a compromised DPU. Hardware trust. Waits on Tamper evidence for verifier devices 2 5
- Advances in low-communication training could shrink the margin that the cap enforces. Capacity bounds 2 6 7
- 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 9
- State-level attackers who hold the hardware can likely compromise the best current secure enclosures. Hardware trust. Waits on Tamper evidence for verifier devices 9 11
- 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 8 10
- Restricting future rule updates would need a formal language for rules, which the authors judge most likely infeasible for early flexHEG versions. Protocol soundness 8
- 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 10
- On-chip telemetry from timing, memory and performance counters
- Shipping accelerators need a tamper-resistant, authenticated telemetry path. Hardware trust. Waits on Hardware-enabled guarantees (flexHEG) and guarantee processors 15 16
- NVIDIA's full confidential-computing mode disables the hardware performance counters its profiling tools use, so telemetry that needs them conflicts with it. Privacy & leakage 17 18
- Continuous challenge puzzles cost power and throughput on production workloads. Performance & compatibility 14
- Evaluation has not gone beyond single nodes, framework-level evasion and one vendor's hardware. Adversarial validation 16
- 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
- No measured detection or false-alarm rates for finding undeclared facilities have been published. Adversarial validation 19 21
- Recent high-resolution imagery is costly, is limited by weather and needs trained analysts. Access & governance 21
- Bandwidth limits and compartmentalization
What the verifier sees1 depend on design
From the family or selected implementation's record.
- Model weights
Depends on the design for On-chip telemetry from timing, memory and performance counters.
Hidden by Hardware-enabled guarantees (flexHEG) and guarantee processors.
Not involved: Bandwidth limits and compartmentalization and Remote detection of data centres.
- Inputs and outputs
Depends on the design for On-chip telemetry from timing, memory and performance counters.
Hidden by Hardware-enabled guarantees (flexHEG) and guarantee processors.
Not involved: Bandwidth limits and compartmentalization and Remote detection of data centres.
- Training data
Depends on the design for On-chip telemetry from timing, memory and performance counters.
Hidden by Hardware-enabled guarantees (flexHEG) and guarantee processors.
Not involved: Bandwidth limits and compartmentalization and Remote detection of data centres.
Exposure notes
- Bandwidth limits and compartmentalization: Caps traffic between groups of chips; it does not read the traffic's content.
- Hardware-enabled guarantees (flexHEG) and guarantee processors: The guarantee processor sees the chip's traffic inside a sealed enclosure and reports only whether rules were kept.
- On-chip telemetry from timing, memory and performance counters: Counters do not read weights or data, but richer counters can leak secrets through side channels.
- Remote detection of data centres: Works from outside the facility; it does not handle model data.
Implementations2 systems
- Bandwidth limits and compartmentalization
- Proposed AI 2040 inference-only verification stack Proposed architecture, AI Futures Project
- Proposed RAND secure inference data center (SIDC) design Proposed architecture, RAND
- Hardware-enabled guarantees (flexHEG) and guarantee processors
- None on the map
- On-chip telemetry from timing, memory and performance counters
- None on the map
- Remote detection of data centres
- None on the map
Sources23 cited
- Verification Plan, R. Dean (2026). Original
- Traffic Shaping for Workload Classification, Lucid Computing (2026). Original
- A System Overview for Near-Term, Low-Trust AI Compute Verification, N. Cankaya (2026). Original
- De-risking Interconnect Limits for AI Verification, A. Scher et al. (2026). Original
- The Tray as a Bandwidth Boundary, Amodo Design (2026). Original
- DiLoCo: Distributed Low-Communication Training of Language Models, A. Douillard et al. (2024). Original
- Does Distributed Training Undermine Compute Governance?, R. Rahman (2026). Original
- Flexible Hardware-Enabled Guarantees for AI Compute, J. Petrie et al. (2025). Original
- Technical Options for Flexible Hardware-Enabled Guarantees, J. Petrie & O. Aarne (2025). Original
- International Security Applications of Flexible Hardware-Enabled Guarantees, O. Aarne & J. Petrie (2025). Original
- 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). Original
- Secure, Governable Chips: Using On-Chip Mechanisms to Manage National Security Risks from AI & Advanced Computing, O. Aarne et al. (2024). Original
- Hardware-Enabled Mechanisms for Verifying Responsible AI Development, A. O'Gara et al. (2025). Original
- Timing and Memory Telemetry on GPUs for AI Governance, S. K. Monfared et al. (2026). Original
- Guaranteeable Memory: An HBM-Based Chiplet for Verifiable AI Workloads, J. Petrie (2025). Original
- Detecting Hidden ML Training With Zero-Overhead Telemetry, R. Rahman & S. Tajdari (2026). Original
- On TEEs for Privacy-Preserving Monitoring in AI Governance, Gloria Z (2026). Original
- NVIDIA Secure AI with Blackwell and Hopper GPUs (White Paper), NVIDIA (2025). Original
- Covert AI Projects, B. Halstead & T. Larsen (2026). Original
- Verifying International Agreements on AI: Six Layers of Verification for Rules on Large-Scale AI Development and Deployment, M. Baker et al. (2025). Original
- Tracking Hyperscale AI Data Center Growth with Satellite Imagery, C. Krawec (2026). Original
- Introducing the Frontier Data Centers Hub, Epoch AI (2025). Original
- AI Data Centers Documentation – Methodology, Epoch AI (2026). Original
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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 development status
Development status
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, development status, security evidence, assumptions and findings.
Mechanisms
A mechanism is a general technique for verifying claims. Its badge is its development status 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. Failure counts are per mechanism. Summary counts name mechanisms with open failures, not a sum of attacks. Choosing an implementation narrows each row to that record's assessed use; family findings remain as context. 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.
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 failure or a dependency. They are pointers, not recommendations: each brings its own readiness level and findings, and none is claimed to close a failure. Links from failures 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) design2 mechanismsProposed architecture, RAND