Proposal Explorer

Add claims to see which ones the mechanisms address.

Operational use

• Operational use Hardware-attested weight binding for hardware-attested weight binding showing users that a service runs its committed weights

Built for an adversarial prover

Whole-workload recomputation (reproducible packets)

No new hardware needed

Hardware-attested weight binding

Failures since mitigated

• Launch-state attestation does not by itself cover weights loaded later in Hardware-attested weight binding ^{9 19}

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.

• Hardware-attested weight binding: Independent red-team

Open critical failures

• Underlying attestation can be forged or relayed in Hardware-attested weight binding

  Critical for weight binding against an operator with physical access to affected hardware, or with control of the hypervisor on an AMD SEV-SNP platform without AMD's fixes. 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. ^{10 11 15 16 17 18}

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

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

  Related mechanism Proposed 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 failure is mitigated. Add

Family findings

• Whole-workload recomputation (reproducible packets)

  Context for AI 2040 inference-only verification stack. Findings from the mechanism family appear here as context. They apply to an implementation only when its own record lists them, under the conditions stated there.

  • Spare compute is outside the scheme in Whole-workload recomputation (reproducible packets)

    The plan states that it does not verify that spare compute is not used for unapproved workloads, because this seems very challenging. Recomputation checks the correctness of declared work, not its completeness. ^{1 8}

    Scope limitation · Theoretical argument. On the record

    Related mechanism Proposed Proofs of useful work for capacity accounting: Proposed as one input to accounting for spare capacity on declared hardware. A pointer, not evidence that this failure is mitigated. Add

  • Non-compliant work could be encoded inside compliant-looking packets in Whole-workload recomputation (reproducible packets)

    The plan notes that an AI company might try to encode a non-compliant workload inside a workload that looks compliant on the surface. ¹

    Open question · Theoretical argument. On the record

Open significant failures

1 failure in 1 mechanism

• A recomputation family degrades against prompt-controlling adversaries in AI 2040 inference-only verification stack

  The plan's companion page names DiFR among the recomputation schemes being tested. An independent study found that Gumbel-based inference verification, the family that includes Token-DiFR, leaks roughly twice as many bits per token when the adversary chooses prompts that disrupt the text's structure, across six models of 1 to 32 billion parameters. The slowdown it imposes on a hidden-signalling adversary falls from 146–254 times under benign prompts to 60–118 times. The attack weakens the bound on hidden information in outputs, which the plan relies on to keep undeclared results from leaving. ^{1 2 6 7}

  Known failure · Demonstrated attack · Significant · Open. On the record

Scope limitations

• The recomputation server must be trusted in AI 2040 inference-only verification stack

  The plan calls the integrity of the recomputation server an extremely important aspect, and its argument that sampling verifies all outputs assumes that the server's computations and outputs can be trusted. The companion page notes that the server sits inside the prover's facility, possibly under the prover's physical control, and that hardening it against integrity attacks needs significant research. Amodo rates recomputation-server security as not on track. ^{1 2 4}

  Scope limitation · Theoretical argument. On the record

• Spare compute is not verified in AI 2040 inference-only verification stack

  The plan states that it does not verify that spare compute is unused for unapproved workloads, because this seems very challenging. It relies instead on side-channel bounds and memory wipes, so that the only results that persist are verified inference outputs. ¹

  Scope limitation · Theoretical argument. On the record

• For private models, a user can confirm consistency but not content in Hardware-attested weight binding

  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. ^{9 20}

  Scope limitation · Open question. On the record

Not yet demonstrated

Proposed Whole-workload recomputation (reproducible packets)

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

• Operational use TEE remote attestation for AI workloads Add
  • Hardware-attested weight binding waits on it. Attestation that resists physical attackers, for the enclave variant.
• Research demo Tamper evidence for verifier devices Add
  • Whole-workload recomputation (reproducible packets) waits on it. Checking that taps are correctly installed and stay in place at scale is not a solved problem, and hardening the recomputation server inside the prover's facility needs significant research.
• Proposed Memory wiping and proofs of secure erasure Add
  • Whole-workload recomputation (reproducible packets) waits on it. Memory wiping may use existing algorithms, but hardware testing is at an early stage.
• Proposed Network taps and certifiers Add
  • Whole-workload recomputation (reproducible packets) waits on it. Passive optical taps work at 400G, but the 800G and 1600G line rates now arriving in data centres are undemonstrated.
• Proposed Side-channel suppression for isolated facilities Add
  • Whole-workload recomputation (reproducible packets) waits on it. There is no plan yet for quickly scaling side-channel defences on a frontier cluster; only early theoretical pieces exist.
• Operational use Sampled inference recomputation Add
  • Whole-workload recomputation (reproducible packets) depends on it.
• Proposed Hardware-enabled guarantees (flexHEG) and guarantee processors Add

  Excluded by filters: needs new chips

  • Bears on the open critical failure “Underlying attestation can be forged or relayed” in Hardware-attested weight binding. A tamper-protected enclosure around the chip is the proposed answer when the party that holds the hardware may attack it physically.

Missing prerequisites

• Operational use Sampled inference recomputation needed by Whole-workload recomputation (reproducible packets) Add
• Operational use TEE remote attestation for AI workloads needed by Hardware-attested weight binding Add

Blockers

7 blockers recorded

• Whole-workload recomputation (reproducible packets)
  • A fully reproducible inference stack needs substantial software and tooling, and per-packet network reproducibility may need considerable software, firmware and possibly hardware work. Performance & compatibility. Waits on Whole-workload recomputation (reproducible packets) ²
  • Passive optical taps work at 400G, but the 800G and 1600G line rates now arriving in data centres are undemonstrated. Performance & compatibility. Waits on Network taps and certifiers ²
  • Checking that taps are correctly installed and stay in place at scale is not a solved problem, and hardening the recomputation server inside the prover's facility needs significant research. Hardware trust. Waits on Tamper evidence for verifier devices ^{2 4}
  • There is no plan yet for quickly scaling side-channel defences on a frontier cluster; only early theoretical pieces exist. Coverage & hidden compute. Waits on Side-channel suppression for isolated facilities ²
  • Memory wiping may use existing algorithms, but hardware testing is at an early stage. Coverage & hidden compute. Waits on Memory wiping and proofs of secure erasure ²
  • Robust red-teaming of recomputation schemes has not started, and most algorithm development remains academic. Adversarial validation ^{2 4}
• Hardware-attested weight binding
  • Attestation that resists physical attackers, for the enclave variant. Hardware trust. Waits on TEE remote attestation for AI workloads ¹⁵

From the family or selected implementation's record. ?

Model weights

Hidden by Hardware-attested weight binding.

Unspecified for Whole-workload recomputation (reproducible packets). Check the implementation record.

Inputs and outputs

Unspecified for Whole-workload recomputation (reproducible packets) and Hardware-attested weight binding. Check the implementation record.

Training data

Not involved: Hardware-attested weight binding.

Unspecified for Whole-workload recomputation (reproducible packets). Check the implementation record.

Whole-workload recomputation (reproducible packets)

• Proposed AI 2040 inference-only verification stack Proposed architecture, AI Futures Project

Hardware-attested weight binding

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

1. Verification Plan, R. Dean (2026). Original
2. Get Involved in Verification, AI Futures Project (2026). Original
3. Verifying international AI deals: Plan A, the state-of-play, and what you can do to help, T. Milton et al. (2026). Original
4. AI 2040 Plan A — Verification SITREP, Amodo Design (2026). Original
5. Fitting a Network TAP to our Inference Verification Prototype, Amodo Design (2026). Original
6. Verifying LLM Inference to Detect Model Weight Exfiltration, R. Rinberg et al. (2025). Original
7. Adversarial Entropy Inflation Against Gumbel-Based Inference Verification, N. Kezins (2026). Original
8. Example Schemes for Verifying High-Stakes AI Agreements, Amodo Design (2026). Original
9. How Tinfoil Proves Exactly What Model Is Running, Tinfoil Team (2026). Original
10. PAL*M: Property Attestation for Large Generative Models, P. Chantasantitam et al. (2026). Original
11. A primer on secure enclaves, Tinfoil (2026). Original
12. Backend infrastructure, Tinfoil (2026). Original
13. How verification works in Tinfoil, Tinfoil (2026). Original
14. modelwrap: Reproducible dm-verity read-only image of Huggingface models, Tinfoil (2026). Original
15. TEE.fail: Breaking Trusted Execution Environments via DDR5 Memory Bus Interposition, J. Chuang et al. (2026). Original
16. Battering RAM: Low-Cost Interposer Attacks on Confidential Computing via Dynamic Memory Aliasing, J. De Meulemeester et al. (2026). Original
17. RMPocalypse: How a Catch-22 Breaks AMD SEV-SNP, B. Schlüter & S. Shinde (2025). Original
18. SEV-SNP RMP Initialization Vulnerability (AMD-SB-3020), AMD (2025). Original
19. On TEEs for Privacy-Preserving Monitoring in AI Governance, Gloria Z (2026). Original
20. Attestable Audits: Verifiable AI Safety Benchmarks Using Trusted Execution Environments, C. Schnabl et al. (2025). Original