Proposal Explorer

Add claims to see which ones the mechanisms address.

In production

• R3 Deterministic and bit-exact inference for reproducing open-model inference from receipts in Gensyn's information-market service
• R3 Model identity attestation for showing clients that the served weights match a committed hash

Built for an adversarial prover

Deterministic and bit-exact inference

No new hardware needed

Deterministic and bit-exact inference and Model identity attestation

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

• Deterministic and bit-exact inference: Analysis
• Model identity attestation, Tinfoil model identity (Modelwrap): Analysis

Open critical flaws

• Inherits attacks on the underlying TEEs in Tinfoil model identity (Modelwrap)

  Critical when model identity must hold against an operator with physical access to an affected host. Tinfoil documents physical attacks as an enclave limitation. These are hardware-class demonstrations, not a published break of Modelwrap or Tinfoil's deployed verification chain.

  Tinfoil's model commitment and boot-time GPU check depend on the CPU attestation. The TEE findings distinguish Intel TDX forgery on DDR5, AMD SEV-SNP forgery on DDR4 in Battering RAM, and software-only RMPocalypse on platforms lacking AMD's fixes. TEE.fail recovered a guest OpenSSL key on AMD, not an AMD attestation key. Its GPU relay demonstration used an H100 with forged TDX evidence; it does not establish the same result for Tinfoil's H200 or B200 configurations. ^{13 14 17 18 19 20}

  Response: Tinfoil acknowledges the physical-access boundary. The TEE.fail authors report that Intel and AMD treat interposer attacks as outside their threat models and recommend physically secure servers. AMD reports firmware fixes for RMPocalypse.

  Demonstrated attack · Critical · Open · Inherited finding. On the record · Related finding in TEE remote attestation for AI workloads

Family findings

• Model identity attestation

  Context for Tinfoil model identity (Modelwrap). These findings concern the family or other implementations; applicability must be checked against their stated scope.

  • Underlying attestation can be forged or relayed in Model identity attestation

    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. ^{13 17 18 19 20 21}

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

    Demonstrated attack · Critical · Open · Inherited finding. On the record · Related finding in TEE remote attestation for AI workloads

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

  • Launch-state attestation does not by itself cover weights loaded later in Model identity attestation

    Attestation measures launch state, and weights are read from disk after boot. A signature checked at load time does not stop a malicious hypervisor from altering the disk afterwards. Tinfoil reports mitigating this with dm-verity checks on every read. Unmeasured runtime configuration remains a general risk. ^{12 22}

    Theoretical argument · Significant · Mitigated. On the record

  • For private models, a user can confirm consistency but not content in Model identity attestation

    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. ^{12 23}

    Open question · Significant · Open. On the record

  • Recomputation depends on trusted logging and randomness, and its tolerance leaves a covert channel in Model identity attestation

    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. ^{24 25}

    Demonstrated attack · Significant · Open. On the record

    Related mechanism R1 Network taps and certifiers: 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. A pointer, not evidence that this flaw is mitigated. Add

    Related mechanism R3 Deterministic and bit-exact inference: Bit-exact inference would remove the numerical tolerance that leaves this channel. A pointer, not evidence that this flaw is mitigated. In the proposal.

Open significant flaws

3 flaws in 2 mechanisms

• Cross-hardware replay relies on reverse-engineered, closed behaviour in Deterministic and bit-exact inference

  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. ^{1 3}

  Open question · Significant · Open. On the record

• Side channels, I/O leakage and denial of service are outside enclave protection in Tinfoil model identity (Modelwrap)

  Tinfoil's documentation lists timing, power and electromagnetic side channels, host observation of access patterns and I/O, denial of service, supply-chain compromise and rollback as limitations. ¹³

  Theoretical argument · Significant · Open. On the record

• Private models can be checked only for consistency in Tinfoil model identity (Modelwrap)

  For unpublished weights, the root hash appears in the attestation without the weights being exposed. Users can then confirm only that they get the same model each time. ¹²

  Open question · Significant · Open. On the record

Open minor flaws

1 mechanism with minor findings

• Some kernels remain genuinely nondeterministic in Deterministic and bit-exact inference

  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. ¹

  Open question · Minor · Open. On the record

Mechanisms on the map that are not in the proposal. Pointers, not recommendations. ?

• R3 TEE remote attestation for AI workloads Add
  • Model identity attestation waits on it. The underlying TEE attestation does not resist attackers with physical access to the host.

Missing prerequisites

• R3 TEE remote attestation for AI workloads needed by Model identity attestation Add

Blockers

6 blockers recorded

• Deterministic and bit-exact inference
  • Batch-invariant kernels cost throughput: in Thinking Machines' Qwen3-8B test, an improved deterministic build took 42 s against 26 s for vLLM's default, and SGLang reports an average 34.35% slowdown on its FlashInfer and FlashAttention 3 backends. Performance & compatibility ^{2 4}
  • Coverage is incomplete: the bit-exact emulator targets dense blocks on NVIDIA GPUs and excludes mixture-of-experts inference and training, and vLLM's batch-invariant mode is in beta, with open work on AMD hardware and speculative decoding. Performance & compatibility ^{1 5 26}
  • Amodo's status page for the AI 2040 verification plan rates a reproducible inference stack for that plan as 'not started'. Performance & compatibility ²⁷
  • Exact replay requires the prover to disclose weights, software versions, parallelism and batch sizes to whoever recomputes. Privacy & leakage ^{1 11}
• Model identity attestation
  • The underlying TEE attestation does not resist attackers with physical access to the host. Hardware trust. Waits on TEE remote attestation for AI workloads ^{13 17}
  • No independent evaluation of the model-identity chain has been published. Adversarial validation

From the family or selected implementation's record. ?

Model weights

Depends on the design for Deterministic and bit-exact inference and Model identity attestation.

Inputs and outputs

Depends on the design for Deterministic and bit-exact inference.

Unspecified for Model identity attestation. Check the implementation record.

Training data

Not involved: Deterministic and bit-exact inference and Model identity attestation.

Deterministic and bit-exact inference

• R2 Batch-invariant inference kernels (Thinking Machines) Open-source project, Thinking Machines Lab
• R3 Verde and RepOps (Gensyn) Product, Gensyn
• R1 Low-trust AI compute verification system overview Proposed architecture, Machine Intelligence Research Institute

Model identity attestation

• R2 Attestable Audits Research prototype, University of Cambridge
• R2 PAL*M Research prototype, University of Waterloo
• R3 Tinfoil model identity (Modelwrap) Product, Tinfoil

1. Bit-Exact AI Inference Verification Without Performance Tradeoffs, N. Cankaya (2026). Original
2. Defeating Nondeterminism in LLM Inference, H. He & Thinking Machines Lab (2025). Original
3. Hawkeye: Reproducing GPU-Level Non-Determinism, E. Badash et al. (2026). Original
4. Towards Deterministic Inference in SGLang and Reproducible RL Training, The SGLang Team (2025). Original
5. Batch Invariance (vLLM documentation), vLLM project (2026). Original
6. gensyn-ai/ree: Gensyn Reproducible Execution Environment (GitHub repository), Gensyn (2026). Original
7. EigenCloud Brings Verifiable AI to Mass Market with EigenAI and EigenCompute Launches, EigenCloud (2025). Original
8. Building Delphi: Pricing, Settlement, and Agentic Trading, D. Jedamski (2026). Original
9. Reproducible Execution Environment (REE) (Gensyn documentation), Gensyn (2026). Original
10. What is Delphi? (Delphi documentation), Gensyn (2026). Original
11. A System Overview for Near-Term, Low-Trust AI Compute Verification, N. Cankaya (2026). Original
12. How Tinfoil Proves Exactly What Model Is Running, Tinfoil Team (2026). Original
13. A primer on secure enclaves, Tinfoil (2026). Original
14. Backend infrastructure, Tinfoil (2026). Original
15. How verification works in Tinfoil, Tinfoil (2026). Original
16. modelwrap: Reproducible dm-verity read-only image of Huggingface models, Tinfoil (2026). Original
17. TEE.fail: Breaking Trusted Execution Environments via DDR5 Memory Bus Interposition, J. Chuang et al. (2026). Original
18. Battering RAM: Low-Cost Interposer Attacks on Confidential Computing via Dynamic Memory Aliasing, J. De Meulemeester et al. (2026). Original
19. RMPocalypse: How a Catch-22 Breaks AMD SEV-SNP, B. Schlüter & S. Shinde (2025). Original
20. SEV-SNP RMP Initialization Vulnerability (AMD-SB-3020), AMD (2025). Original
21. PAL*M: Property Attestation for Large Generative Models, P. Chantasantitam et al. (2026). Original
22. On TEEs for Privacy-Preserving Monitoring in AI Governance, Gloria Z (2026). Original
23. Attestable Audits: Verifiable AI Safety Benchmarks Using Trusted Execution Environments, C. Schnabl et al. (2025). Original
24. Verifying LLM Inference to Detect Model Weight Exfiltration, R. Rinberg et al. (2025). Original
25. Adversarial Entropy Inflation Against Gumbel-Based Inference Verification, N. Kezins (2026). Original
26. [Feature]: Batch Invariant Feature and Performance Optimization (vLLM issue #27433), vLLM project contributors (2025). Original
27. AI 2040 Plan A — Verification SITREP, Amodo Design (2026). Original