# 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-0012,M-0002&implementations=M-0002: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 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Model identity attestation | R3 | Semi-trusted | Independent red-team | Existing features | 1 / 2 / 0 | depends | depends | not involved |
| Deterministic and bit-exact inference / Low-trust AI compute verification system overview | R1 | Adversarial | Analysis | Retrofit device | 0 / 2 / 1 | unspecified | unspecified | unspecified |

## Claims

No claims chosen.

## Mechanisms

### 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.

### Deterministic and bit-exact inference

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. ([Deterministic and bit-exact inference](https://trustbutveri.fyi/mechanisms/deterministic-inference/))

- 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.


## 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**

- Deterministic and bit-exact inference

**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) [1][13]


## 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**

- Model identity attestation: Independent red-team
- Deterministic and bit-exact inference / Low-trust AI compute verification system overview: Analysis


## 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) [2][4][8][10][11][12]. 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**

- 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) [1][14]. 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) [3][9]. 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 (R1, in the proposal). Bit-exact inference would remove the numerical tolerance that leaves this channel.
- 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) [15]. 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) [15]. 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.

**Family finding context**

- Context for Low-trust AI compute verification system overview; applicability depends on the finding's scope. Some kernels remain genuinely nondeterministic (open question, in Deterministic and bit-exact inference; https://trustbutveri.fyi/mechanisms/deterministic-inference/#flaw-1) [17]. The bit-exact work separates kernels that are deterministic but not batch-invariant from truly nondeterministic ones that use atomic functions. Some integer de-quantization kernels use atomic additions and remain nondeterministic, so exact replay needs backends that avoid them.
- Context for Low-trust AI compute verification system overview; applicability depends on the finding's scope. Cross-hardware replay relies on reverse-engineered, closed behaviour (open question, in Deterministic and bit-exact inference; https://trustbutveri.fyi/mechanisms/deterministic-inference/#flaw-2) [17][18]. Emulating one GPU's rounding on another requires reverse-engineering tensor-core arithmetic and modelling proprietary kernel choices. Hawkeye covers a subset of NVIDIA architectures and states that attention and other higher-level operations need further reverse engineering. For the bit-exact emulator, a proprietary Hopper kernel family is an open edge case.

**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) [15]. 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**

- Deterministic and bit-exact inference: 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.
- **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.
  - Deterministic and bit-exact inference 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)
  - Deterministic and bit-exact inference 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)
  - Deterministic and bit-exact inference 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.
- **Side-channel suppression for isolated facilities** (R1 Proposed, assessed for bounding physical covert channels out of a verified enclosure)
  - Deterministic and bit-exact inference waits on it: A mass-manufacturable, good-enough side-channel defence, particularly power-line filtering, has not been constructed or red-teamed.


## 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 Deterministic and bit-exact inference

**Blockers**

- Model identity attestation: Attestation that resists physical attackers, for the enclave variant. (hardware trust; waits on TEE remote attestation for AI workloads) [8]
- 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) [3]
- Model identity attestation: The recomputation variant needs the verifier to hold the declared weights. (access & governance) [3]
- Deterministic and bit-exact inference: 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) [15]
- Deterministic and bit-exact inference: 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) [15]
- Deterministic and bit-exact inference: 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) [15]
- Deterministic and bit-exact inference: 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) [15]
- Deterministic and bit-exact inference: 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) [15]
- Deterministic and bit-exact inference: The threat model is under-developed and needs input from cybersecurity and AI threat-modelling experts. (adversarial validation) [15]


## 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 Deterministic and bit-exact inference.
- Inputs and outputs: shown by none; depends on the design for Model identity attestation; hidden by none; not involved in none; unspecified for Deterministic and bit-exact inference.
- Training data: shown by none; depends on the design for none; hidden by none; not involved in Model identity attestation; unspecified for Deterministic and bit-exact inference.

## Implementations

- 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)
- Deterministic and bit-exact inference: [Batch-invariant inference kernels (Thinking Machines)](https://trustbutveri.fyi/implementations/batch-invariant-inference-kernels/) (R2, open-source project); [Verde and RepOps (Gensyn)](https://trustbutveri.fyi/implementations/gensyn-verde-repops/) (R3, product); [Low-trust AI compute verification system overview](https://trustbutveri.fyi/implementations/low-trust-compute-verification-system-overview/) (R1, proposed architecture)

## Sources

1. How Tinfoil Proves Exactly What Model Is Running, Tinfoil Team (2026). https://tinfoil.sh/blog/2026-02-03-proving-model-identity
2. PAL*M: Property Attestation for Large Generative Models, P. Chantasantitam et al. (2026). https://arxiv.org/abs/2601.16199
3. Verifying LLM Inference to Detect Model Weight Exfiltration, R. Rinberg et al. (2025). https://arxiv.org/abs/2511.02620
4. A primer on secure enclaves, Tinfoil (2026). https://docs.tinfoil.sh/verification/secure-enclave-primer
5. Backend infrastructure, Tinfoil (2026). https://docs.tinfoil.sh/verification/attestation-architecture
6. How verification works in Tinfoil, Tinfoil (2026). https://docs.tinfoil.sh/verification/verification-in-tinfoil
7. modelwrap: Reproducible dm-verity read-only image of Huggingface models, Tinfoil (2026). https://github.com/tinfoilsh/modelwrap
8. TEE.fail: Breaking Trusted Execution Environments via DDR5 Memory Bus Interposition, J. Chuang et al. (2026). https://tee.fail/
9. Adversarial Entropy Inflation Against Gumbel-Based Inference Verification, N. Kezins (2026). https://arxiv.org/abs/2608.23375
10. Battering RAM: Low-Cost Interposer Attacks on Confidential Computing via Dynamic Memory Aliasing, J. De Meulemeester et al. (2026). https://batteringram.eu/
11. RMPocalypse: How a Catch-22 Breaks AMD SEV-SNP, B. Schlüter & S. Shinde (2025). https://rmpocalypse.github.io/
12. SEV-SNP RMP Initialization Vulnerability (AMD-SB-3020), AMD (2025). https://www.amd.com/en/resources/product-security/bulletin/amd-sb-3020.html
13. 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
14. Attestable Audits: Verifiable AI Safety Benchmarks Using Trusted Execution Environments, C. Schnabl et al. (2025). https://arxiv.org/abs/2506.23706
15. 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
16. Fingerprinting All AI Cluster I/O Without Mutually Trusted Processors, N. Cankaya et al. (2026). https://arxiv.org/abs/2606.10724
17. Bit-Exact AI Inference Verification Without Performance Tradeoffs, N. Cankaya (2026). https://arxiv.org/abs/2606.00279
18. Hawkeye: Reproducing GPU-Level Non-Determinism, E. Badash et al. (2026). https://proceedings.mlsys.org/paper_files/paper/2026/hash/e217c271a57c365a246b0ad39e668ba8-Abstract-Conference.html