# AI verification proposal

A proposal built with the Proposal Explorer of the AI Verification Tech Map (https://trustbutveri.fyi/), from its records of 2026-10-08. Interactive version: https://trustbutveri.fyi/explorer/?mechanisms=M-0022,M-0012&implementations=M-0022:I-0012

How to read it: a claim is something one party wants to verify about another's AI hardware or software. A mechanism is a general technique for verifying claims; it is "aimed at" a claim when that is its direct purpose, and "supporting" when it contributes without being aimed at it. A claim is addressed when a mechanism in the proposal is aimed at it and is not excluded by the filters; addressed does not mean verified, so check that mechanism's readiness and open flaws. Readiness levels R0 to R4 describe one record's public evidence for its assessed use and are never combined. Definitions: https://trustbutveri.fyi/about/methodology/ (roles, properties and flaws) and https://trustbutveri.fyi/about/readiness/ (readiness levels).

## Filters

Filters apply to mechanisms only and describe the setting the proposal is for.

None set. Every mechanism on the map was available.

## Overview

One row per mechanism, read from its record. Open flaws: critical / significant / minor. The last three columns are the editors' reading of what the verifier sees.

| Mechanism | Readiness | Prover | Attack testing | Hardware | Open flaws | Weights | Inputs and outputs | Training data |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Side-channel suppression for isolated facilities / Low-trust AI compute verification system overview | R1 | Adversarial | Analysis | Retrofit device | 0 / 2 / 1 | unspecified | unspecified | unspecified |
| Model identity attestation | R3 | Semi-trusted | Independent red-team | Existing features | 1 / 2 / 0 | depends | depends | not involved |

## Claims

No claims chosen.

## Mechanisms

### Side-channel suppression for isolated facilities

A retrofittable reference design in which network taps commit to all facility traffic, and air-gapped, independently sourced checkers later re-run randomly challenged records. ([Side-channel suppression for isolated facilities](https://trustbutveri.fyi/mechanisms/side-channel-suppression/))

- Assessment: selected implementation [Low-trust AI compute verification system overview](https://trustbutveri.fyi/implementations/low-trust-compute-verification-system-overview/).
- Readiness: R1 Proposed, assessed for screening challenged records to show declared inference compute is not training.
- Claims in this proposal: none of them.
- Threat model: adversarial prover. Hardware: retrofit device. Prover cooperation: required. Attack testing: analysis. Category: Isolation & system architectures.
- What the verifier sees: model weights unspecified; inputs and outputs unspecified; training data unspecified. This Explorer has no asset-specific exposure assessment for this implementation. Check its source and deployment assumptions.

### Model identity attestation

Establishes that responses come from a specific, committed set of model weights, using enclave measurements or recomputation of sampled outputs. ([Model identity attestation](https://trustbutveri.fyi/mechanisms/model-identity-attestation/))

- Assessment: mechanism family.
- Readiness: R3 In production, assessed for showing users that a service runs the declared model weights.
- Claims in this proposal: none of them.
- Threat model: semi-trusted prover. Hardware: existing features. Prover cooperation: required. Attack testing: independent red-team. Category: Cryptographic & computational.
- What the verifier sees: model weights depends; inputs and outputs depends; training data not involved. The enclave route shows only hashes; the recomputation route gives the verifier the weights and the sampled requests and responses.


## Properties

**In production**

- Model identity attestation: R3 In production, assessed for showing users that a service runs the declared model weights

**Built for an adversarial prover**

- Side-channel suppression for isolated facilities

**No new hardware needed**

- Model identity attestation

**Flaws since mitigated**

- Launch-state attestation does not by itself cover weights loaded later (in Model identity attestation) [4][16]


## Attack testing

Published attempts to break a system, including those that found failures. Testing history does not establish that open flaws are resolved.

**Testing history**

- Side-channel suppression for isolated facilities / Low-trust AI compute verification system overview: Analysis
- Model identity attestation: Independent red-team


## Limits

**Open critical flaws**

- Underlying attestation can be forged or relayed (demonstrated attack, in Model identity attestation; https://trustbutveri.fyi/mechanisms/model-identity-attestation/#flaw-1) [5][7][11][13][14][15]. Inherited finding. Critical for the enclave route against an operator with physical access to affected hardware, or control of an unpatched SEV-SNP hypervisor. It does not apply to the recomputation route. PAL*M excludes physical attacks, and Tinfoil acknowledges this boundary. The enclave route inherits the platform-specific TEE attestation failures. Intel TDX forgery and H100 relay were demonstrated with physical access and host control. Battering RAM defeated AMD SEV-SNP attestation on DDR4 servers; RMPocalypse did so from malicious host software on platforms without AMD's fixes. These demonstrate failures of the trust roots, not of each model-commitment protocol. Related finding: https://trustbutveri.fyi/mechanisms/tee-remote-attestation/#flaw-1.

  Response: The TEE.fail authors report that physical interposer attacks are outside Intel's and AMD's threat models. AMD reports fixes for RMPocalypse.

  Related mechanism: Hardware-enabled guarantees (flexHEG) and guarantee processors (R1, not in the proposal). A tamper-protected enclosure around the chip is the proposed answer when the party that holds the hardware may attack it physically.

**Open significant flaws**

- Mismatches cannot be attributed to cheating or error (theoretical argument, in Low-trust AI compute verification system overview; https://trustbutveri.fyi/implementations/low-trust-compute-verification-system-overview/#flaw-1) [1]. 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.
- Inspector agents may be manipulable (open question, in Low-trust AI compute verification system overview; https://trustbutveri.fyi/implementations/low-trust-compute-verification-system-overview/#flaw-3) [1]. 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.
- For private models, a user can confirm consistency but not content (open question, in Model identity attestation; https://trustbutveri.fyi/mechanisms/model-identity-attestation/#flaw-3) [4][17]. When weights are not published, users can check that the same root hash is served each time, but not what the model is. Pairing the hash with an attested evaluation, as in Attestable Audits, is one proposed remedy.
- Recomputation depends on trusted logging and randomness, and its tolerance leaves a covert channel (demonstrated attack, in Model identity attestation; https://trustbutveri.fyi/mechanisms/model-identity-attestation/#flaw-4) [6][12]. The recomputation variant assumes that every input, output and seed is logged correctly, and that the attacker can neither predict nor manipulate which messages are sampled for verification. Legitimate nondeterminism concentrates at a few token positions, and slow leaks within the tolerated slack remain possible. An independent study showed that an adversary who controls the prompts roughly doubles the bits leaked per token, reducing the exfiltration slowdown from 146–254 times under benign prompts to 60–118 times. The attack targets the exfiltration bound, not the check that outputs match the declared model.

  Related mechanism: Network taps and certifiers (R1, not in the proposal). Taps are proposed to copy and hash traffic on the monitored links, reducing reliance on the prover's own log. This still depends on the monitored boundary and trusted capture.

  Related mechanism: Deterministic and bit-exact inference (R3, not in the proposal). Bit-exact inference would remove the numerical tolerance that leaves this channel.

**Family finding context**

- Context for Low-trust AI compute verification system overview; applicability depends on the finding's scope. Supply-chain implants may evade inspection (theoretical argument, in Side-channel suppression for isolated facilities; https://trustbutveri.fyi/mechanisms/side-channel-suppression/#flaw-1) [3]. Cankaya identifies malicious hardware embedded deep in purchased components as a residual risk that visual inspection and disassembly may not catch. He notes that radiographic examination under high-security standards could mitigate it.
- Context for Low-trust AI compute verification system overview; applicability depends on the finding's scope. Openings for airflow, power and optics weaken shielding (theoretical argument, in Side-channel suppression for isolated facilities; https://trustbutveri.fyi/mechanisms/side-channel-suppression/#flaw-2) [3]. Cankaya notes that keeping attenuation high while passing high-power airflow, cabling and optical links adds complexity beyond existing shielded-enclosure specifications.
- Context for Low-trust AI compute verification system overview; applicability depends on the finding's scope. Inspection assumptions may not hold (open question, in Side-channel suppression for isolated facilities; https://trustbutveri.fyi/mechanisms/side-channel-suppression/#flaw-3) [3]. The design's statistical argument assumes that visual or disassembly inspection catches every flaw that is present in a sampled unit. Cankaya is unsure whether destructive teardowns are defence-dominant or offence-dominant.

**Open minor flaws**

- Deliberate faults leak a bit each (theoretical argument, in Low-trust AI compute verification system overview; https://trustbutveri.fyi/implementations/low-trust-compute-verification-system-overview/#flaw-2) [1]. 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.

**Not yet demonstrated**

- Side-channel suppression for isolated facilities: R1 Proposed, assessed for screening challenged records to show declared inference compute is not training


## 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. Pointers, not recommendations: each brings its own readiness level and flaws, and none is claimed to close a flaw.

- **Hardware-enabled guarantees (flexHEG) and guarantee processors** (R1 Proposed, assessed for checking and enforcing training-compute limits on chips, against adversaries up to states)
  - Bears on the open critical flaw "Underlying attestation can be forged or relayed" in Model identity attestation. A tamper-protected enclosure around the chip is the proposed answer when the party that holds the hardware may attack it physically.
- **Deterministic and bit-exact inference** (R3 In production, assessed for reproducing open-model inference from receipts in Gensyn's information-market service)
  - Bears on the open significant flaw "Recomputation depends on trusted logging and randomness, and its tolerance leaves a covert channel" in Model identity attestation. Bit-exact inference would remove the numerical tolerance that leaves this channel.
  - Side-channel suppression for isolated facilities waits on it: Exact replay needs complete hardware and software metadata, and the tolerable slowdown from emulation is an open question.
  - Model identity attestation waits on it: Numerical nondeterminism limits how tightly recomputation can pin down the model and sampling.
- **Network taps and certifiers** (R1 Proposed, assessed for committing a complete record of cluster traffic, so declared inference can be checked)
  - Bears on the open significant flaw "Recomputation depends on trusted logging and randomness, and its tolerance leaves a covert channel" in Model identity attestation. Taps are proposed to copy and hash traffic on the monitored links, reducing reliance on the prover's own log. This still depends on the monitored boundary and trusted capture.
  - Side-channel suppression for isolated facilities waits on it: Empirical feasibility of passive optical splitting at 53–112 GBaud under realistic conditions is an open question.
- **TEE remote attestation for AI workloads** (R3 In production, assessed for showing which software ran to a party that distrusts the operator holding the hardware)
  - Model identity attestation waits on it: Attestation that resists physical attackers, for the enclave variant.
- **Tamper evidence for verifier devices** (R2 Demonstrated, assessed for detecting probing of proposed verifier hardware, using server and electronics prototypes as evidence)
  - Side-channel suppression for isolated facilities 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.
- **Timed challenge-response and memory-occupation challenges** (R2 Demonstrated, assessed for detecting whether a GPU is doing other work)
  - Side-channel suppression for isolated facilities 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.


## Dependencies

**Missing prerequisites**

- Network taps and certifiers (R1 Proposed, assessed for committing a complete record of cluster traffic, so declared inference can be checked), needed by Side-channel suppression for isolated facilities
- Deterministic and bit-exact inference (R3 In production, assessed for reproducing open-model inference from receipts in Gensyn's information-market service), needed by Side-channel suppression for isolated facilities

**Shared foundations**

- Deterministic and bit-exact inference, relied on by Side-channel suppression for isolated facilities and Model identity attestation

**Blockers**

- Side-channel suppression for isolated facilities: 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) [1]
- Side-channel suppression for isolated facilities: 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) [1]
- Side-channel suppression for isolated facilities: 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) [1]
- Side-channel suppression for isolated facilities: 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) [1]
- Side-channel suppression for isolated facilities: 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) [1]
- Side-channel suppression for isolated facilities: The threat model is under-developed and needs input from cybersecurity and AI threat-modelling experts. (adversarial validation) [1]
- Model identity attestation: Attestation that resists physical attackers, for the enclave variant. (hardware trust; waits on TEE remote attestation for AI workloads) [11]
- Model identity attestation: Numerical nondeterminism limits how tightly recomputation can pin down the model and sampling. (protocol soundness; waits on Deterministic and bit-exact inference) [6]
- Model identity attestation: The recomputation variant needs the verifier to hold the declared weights. (access & governance) [6]


## What the verifier sees

- Model weights: shown by none; depends on the design for Model identity attestation; hidden by none; not involved in none; unspecified for Side-channel suppression for isolated facilities.
- Inputs and outputs: shown by none; depends on the design for Model identity attestation; hidden by none; not involved in none; unspecified for Side-channel suppression for isolated facilities.
- Training data: shown by none; depends on the design for none; hidden by none; not involved in Model identity attestation; unspecified for Side-channel suppression for isolated facilities.

## Implementations

- Side-channel suppression for isolated facilities: [AI 2040 inference-only verification stack](https://trustbutveri.fyi/implementations/ai-2040-inference-only-verification-plan/) (R1, proposed architecture); [Low-trust AI compute verification system overview](https://trustbutveri.fyi/implementations/low-trust-compute-verification-system-overview/) (R1, proposed architecture); [RAND secure inference data center (SIDC) design](https://trustbutveri.fyi/implementations/rand-secure-inference-data-centers/) (R1, proposed architecture)
- Model identity attestation: [Attestable Audits](https://trustbutveri.fyi/implementations/attestable-audits/) (R2, research prototype); [PAL*M](https://trustbutveri.fyi/implementations/palm/) (R2, research prototype); [Tinfoil model identity (Modelwrap)](https://trustbutveri.fyi/implementations/tinfoil-model-identity/) (R3, product)

## Sources

1. A System Overview for Near-Term, Low-Trust AI Compute Verification, N. Cankaya (2026). https://intelligence.org/wp-content/uploads/2026/06/A-system-overview-for-near-term-low-trust-AI-compute-verification.pdf
2. Fingerprinting All AI Cluster I/O Without Mutually Trusted Processors, N. Cankaya et al. (2026). https://arxiv.org/abs/2606.10724
3. Suppressing Side Channels in an Untrusted Data Center via Retrofitted Defenses, N. Cankaya (2026). https://techgov.intelligence.org/blog/suppressing-side-channels-in-an-untrusted-data-center-via-retrofitted-defenses
4. How Tinfoil Proves Exactly What Model Is Running, Tinfoil Team (2026). https://tinfoil.sh/blog/2026-02-03-proving-model-identity
5. PAL*M: Property Attestation for Large Generative Models, P. Chantasantitam et al. (2026). https://arxiv.org/abs/2601.16199
6. Verifying LLM Inference to Detect Model Weight Exfiltration, R. Rinberg et al. (2025). https://arxiv.org/abs/2511.02620
7. A primer on secure enclaves, Tinfoil (2026). https://docs.tinfoil.sh/verification/secure-enclave-primer
8. Backend infrastructure, Tinfoil (2026). https://docs.tinfoil.sh/verification/attestation-architecture
9. How verification works in Tinfoil, Tinfoil (2026). https://docs.tinfoil.sh/verification/verification-in-tinfoil
10. modelwrap: Reproducible dm-verity read-only image of Huggingface models, Tinfoil (2026). https://github.com/tinfoilsh/modelwrap
11. TEE.fail: Breaking Trusted Execution Environments via DDR5 Memory Bus Interposition, J. Chuang et al. (2026). https://tee.fail/
12. Adversarial Entropy Inflation Against Gumbel-Based Inference Verification, N. Kezins (2026). https://arxiv.org/abs/2608.23375
13. Battering RAM: Low-Cost Interposer Attacks on Confidential Computing via Dynamic Memory Aliasing, J. De Meulemeester et al. (2026). https://batteringram.eu/
14. RMPocalypse: How a Catch-22 Breaks AMD SEV-SNP, B. Schlüter & S. Shinde (2025). https://rmpocalypse.github.io/
15. SEV-SNP RMP Initialization Vulnerability (AMD-SB-3020), AMD (2025). https://www.amd.com/en/resources/product-security/bulletin/amd-sb-3020.html
16. On TEEs for Privacy-Preserving Monitoring in AI Governance, Gloria Z (2026). https://techgov.intelligence.org/blog/on-tees-for-privacy-preserving-monitoring-in-ai-governance
17. Attestable Audits: Verifiable AI Safety Benchmarks Using Trusted Execution Environments, C. Schnabl et al. (2025). https://arxiv.org/abs/2506.23706