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
• Operational use Sampled inference recomputation for checking untrusted workers' activations against the declared model, prompt and precision

Built for an adversarial prover

Network taps and certifiers and Sampled inference recomputation

No new hardware needed

Hardware-attested weight binding and Sampled inference recomputation

Failures since mitigated

• Launch-state attestation does not by itself cover weights loaded later in Hardware-attested weight binding ^{1 11}
• Verifier dictionary attacks on hashes in Network taps and certifiers ¹³

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
• Network taps and certifiers: Analysis
• Sampled inference recomputation: Analysis

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. ^{2 3 7 8 9 10}

  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

Open significant failures

4 failures in 2 mechanisms

• Output nondeterminism leaves covert capacity in Network taps and certifiers

  Hashing cannot remove information hidden in the outputs themselves. The Secure Gateway Device paper estimates that about 0.1 bit per token remains even with seed-synchronized replay checks. For a 200k-GPU inference cluster at full load (2,000 tokens per GPU per second), that is about 40 Mbit/s of covert egress, enough to move a 1 TB model in under three days. The paper names this the core remaining challenge and points to deterministic replay or active scrubbing of hardware-induced entropy. An independent study found that an adversary who chooses the prompts roughly doubles the bits leaked per token under Gumbel-based inference verification; see Bounding unexplained information in outputs. ^{13 21}

  Known failure · Theoretical argument · Significant · Open. On the record

  Related mechanism Operational use Deterministic and bit-exact inference: Deterministic replay is one of the two remedies the flaw's source names. A pointer, not evidence that this failure is mitigated. Add

  Related mechanism Research demo Bounding unexplained information in outputs: Bounds the hidden information outputs can carry by measuring what the declared computation fails to predict. A pointer, not evidence that this failure is mitigated. Add

• Residual side channels in simple passive setups in Network taps and certifiers

  Amodo's analysis of its own tapped prototype lists unvalidated header fields, timing of permitted traffic and variation in response formatting as residual channels, and concludes that the passive tap must be replaced by an active one. ¹⁷

  Known failure · Theoretical argument · Significant · Open. On the record

• Tolerance for numerical noise leaves a covert channel in Sampled inference recomputation

  Schemes that accept approximate matches can put an upper bound on an adversary's covert bandwidth, but they cannot close the channel. The weight-exfiltration detector cut exfiltratable information to under 0.5%, not to zero, on a 30-billion-parameter mixture-of-experts model under benign prompt traffic. Its authors called the channel's size under adversarial prompts an open empirical question. An independent study showed that an adversary who controls the prompts roughly doubles the bits leaked per token. Across six models, that cut the slowdown from 146–254 times under benign prompts to 60–118 times. The attack widens the exfiltration bound. It does not target the check that outputs match the declared model. ^{21 23 33}

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

  Related mechanism Operational use Deterministic and bit-exact inference: Bit-exact inference would remove the numerical tolerance if exact replay can be deployed with the required weights and configuration. A pointer, not evidence that this failure is mitigated. Add

• Some inference optimizations are not covered in Sampled inference recomputation

  TOPLOC's authors state that it cannot detect speculative decoding in which a cheaper model does the decoding. They did not test whether it distinguishes types of key-value (KV) cache compression. DiFR was evaluated only on sampling from a single model. Its authors sketch an extension to one speculative-decoding algorithm but do not test it. ^{24 25}

  Known failure · Theoretical argument · Significant · Open. On the record

Scope limitations

• 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. ^{1 12}

  Scope limitation · Open question. On the record

• Some links cannot be passively tapped in Network taps and certifiers

  Cankaya notes that copper-connected scale-up domains (for example NVL72 racks and TPU v7 cubes) are much harder to tap than fibre, and that optical budgets make passive taps impractical on 400GBASE-SR8 multimode links. Amodo found no taps advertised for 53 GBaud links as of May 2026. ^{14 22}

  Scope limitation · Open question. On the record

• Encrypted fabrics hide plaintext from both parties in Network taps and certifiers

  Cankaya notes that with TEE-protected sessions whose keys are ephemeral and managed inside the TEE, neither the operator nor the manufacturer can recover session keys after the session, so tapped traffic could not be opened for recomputation. For other encrypted fabrics, the operator can retain keys. ¹⁴

  Scope limitation · Open question. On the record

• Completeness rests on physical monitoring left out of scope in Network taps and certifiers

  The Secure Gateway Device paper assumes the facility is physically monitored, and states that the whole architecture depends on the device being the only communication channel. It names radio emanation, power-line signalling and thermal channels as covert channels beyond that scope. ¹³

  Scope limitation · Open question. On the record

  Related mechanism Proposed Side-channel suppression for isolated facilities: Addresses the radio, power-line and thermal channels that network-level designs leave out. A pointer, not evidence that this failure is mitigated. Add

• Only recorded traffic is checked in Sampled inference recomputation

  Recomputation checks that recorded, declared workloads are correct. It cannot show that the record is complete. The published schemes do not cover hidden workloads run on the same compute, or substituted work. Rinberg et al. say their exfiltration-detection scheme cannot stand alone. ^{23 34}

  Scope limitation · Theoretical argument. On the record

  Related mechanism Proposed Network taps and certifiers: Taps copy and hash all traffic on the monitored links, which bears on whether the traffic record is complete. They do not show what else ran on the same chips. A pointer, not evidence that this failure is mitigated. In the proposal.

Open minor failures

1 mechanism with minor failures

• Mixed hardware widens the honest baseline in Sampled inference recomputation

  When honest reference runs span different GPU types, the spread of benign scores grows. In DiFR's tests on Qwen3-30B-A3B, pooling A100 and H200 runs left Token-DiFR unable to separate the two smallest tested changes, a temperature of 1.1 instead of 1.0 and a simulated top-2 sampling bug, at the target false-positive rate, while cross-entropy separated them. Matched provider and verifier environments, or pooling that weights rare large deviations, restored detection. ²⁴

  Known failure · Open question · Minor · Open. On the record

Not yet demonstrated

Proposed Network taps and certifiers

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

• Proposed Hardware-enabled guarantees (flexHEG) and guarantee processors Add
  • 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.
• Operational use Deterministic and bit-exact inference Add
  • Bears on the open significant failure “Output nondeterminism leaves covert capacity” in Network taps and certifiers. Deterministic replay is one of the two remedies the flaw's source names.
  • Bears on the open significant failure “Tolerance for numerical noise leaves a covert channel” in Sampled inference recomputation. Bit-exact inference would remove the numerical tolerance if exact replay can be deployed with the required weights and configuration.
  • Network taps and certifiers waits on it. Nondeterministic inference leaves covert capacity in outputs that hashing cannot remove.
• Research demo Bounding unexplained information in outputs Add
  • Bears on the open significant failure “Output nondeterminism leaves covert capacity” in Network taps and certifiers. Bounds the hidden information outputs can carry by measuring what the declared computation fails to predict.
• 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
  • Network taps and certifiers waits on it. Taps and gateway devices need tamper-evident housing and physical monitoring so that traffic cannot bypass them.
• Proposed Side-channel suppression for isolated facilities Add
  • Network taps and certifiers waits on it. Radio, power-line and thermal channels are not addressed by network-level designs.

Missing prerequisites

• Operational use TEE remote attestation for AI workloads needed by Hardware-attested weight binding Add
• Operational use Deterministic and bit-exact inference needed by Network taps and certifiers Add
• Research demo Tamper evidence for verifier devices needed by Network taps and certifiers Add
• Proposed Side-channel suppression for isolated facilities needed by Network taps and certifiers Add

Blockers

11 blockers recorded

• Hardware-attested weight binding
  • Attestation that resists physical attackers, for the enclave variant. Hardware trust. Waits on TEE remote attestation for AI workloads ⁷
• Network taps and certifiers
  • No complete verification tap has been demonstrated at production frontend link rates, and on the tested CPU no hash algorithm reached line rate with minimum-size frames. Performance & compatibility ^{22 35}
  • Nondeterministic inference leaves covert capacity in outputs that hashing cannot remove. Evidence binding. Waits on Deterministic and bit-exact inference ¹³
  • Taps and gateway devices need tamper-evident housing and physical monitoring so that traffic cannot bypass them. Hardware trust. Waits on Tamper evidence for verifier devices ^{13 15}
  • Radio, power-line and thermal channels are not addressed by network-level designs. Coverage & hidden compute. Waits on Side-channel suppression for isolated facilities ¹³
  • Red-teaming by specialists is called for but has not been reported. Adversarial validation ¹³
• Sampled inference recomputation
  • In tap-based retrofit designs, recording all inference traffic needs network taps and recomputation servers that can ingest it, in the worst case one recomputation-server network interface per inference front-end interface. Coverage & hidden compute. Waits on Network taps and certifiers ^{16 30}
  • In retrofit designs, the recomputation server must sit inside the prover's data centre, possibly under the prover's physical control, and still be protected from a compromised provider, which Amodo rates 'not on track'. Hardware trust ^{23 30}
  • No independent red-team of a recomputation consistency check has been published (the one independent attack study targets the weight-exfiltration bound), and Amodo rates recomputation red-teaming 'not started'. Adversarial validation ^{21 30}
  • Tolerance-based checks need calibration on trusted hardware and exact knowledge of the provider's sampling procedure, and in one prototype a sampling-implementation mismatch produced large spurious differences. Performance & compatibility ^{24 29}
  • The verifier needs the model weights, so checking a closed-weights model requires a trusted, confidential recomputation environment, which the retrofit designs place inside the prover's facility. Privacy & leakage ^{15 24 34}

From the family or selected implementation's record. ?

Model weights

Depends on the design for Network taps and certifiers and Sampled inference recomputation.

Hidden by Hardware-attested weight binding.

Inputs and outputs

Depends on the design for Network taps and certifiers and Sampled inference recomputation.

Unspecified for Hardware-attested weight binding. Check the implementation record.

Training data

Depends on the design for Network taps and certifiers.

Not involved: Hardware-attested weight binding and Sampled inference recomputation.

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

Network taps and certifiers

• Proposed AI 2040 inference-only verification stack Proposed architecture, AI Futures Project
• Proposed Low-trust AI compute verification system overview Proposed architecture, Machine Intelligence Research Institute
• Proposed SASH confidential network logger Research prototype, Singapore AI Safety Hub (SASH)

Sampled inference recomputation

• Proposed AI 2040 inference-only verification stack Proposed architecture, AI Futures Project
• Research demo DiFR (Divergence From Reference) Research prototype
• Proposed Low-trust AI compute verification system overview Proposed architecture, Machine Intelligence Research Institute
• Proposed SASH confidential network logger Research prototype, Singapore AI Safety Hub (SASH)
• Operational use TOPLOC Open-source project, Prime Intellect

1. How Tinfoil Proves Exactly What Model Is Running, Tinfoil Team (2026). Original
2. PAL*M: Property Attestation for Large Generative Models, P. Chantasantitam et al. (2026). Original
3. A primer on secure enclaves, Tinfoil (2026). Original
4. Backend infrastructure, Tinfoil (2026). Original
5. How verification works in Tinfoil, Tinfoil (2026). Original
6. modelwrap: Reproducible dm-verity read-only image of Huggingface models, Tinfoil (2026). Original
7. TEE.fail: Breaking Trusted Execution Environments via DDR5 Memory Bus Interposition, J. Chuang et al. (2026). Original
8. Battering RAM: Low-Cost Interposer Attacks on Confidential Computing via Dynamic Memory Aliasing, J. De Meulemeester et al. (2026). Original
9. RMPocalypse: How a Catch-22 Breaks AMD SEV-SNP, B. Schlüter & S. Shinde (2025). Original
10. SEV-SNP RMP Initialization Vulnerability (AMD-SB-3020), AMD (2025). Original
11. On TEEs for Privacy-Preserving Monitoring in AI Governance, Gloria Z (2026). Original
12. Attestable Audits: Verifiable AI Safety Benchmarks Using Trusted Execution Environments, C. Schnabl et al. (2025). Original
13. Fingerprinting All AI Cluster I/O Without Mutually Trusted Processors, N. Cankaya et al. (2026). Original
14. The Fundamentals and Feasibility of Secure Network Taps for Verifying AI Datacenter Use, N. Cankaya (2026). Original
15. A System Overview for Near-Term, Low-Trust AI Compute Verification, N. Cankaya (2026). Original
16. Verification Plan, R. Dean (2026). Original
17. Fitting a Network TAP to our Inference Verification Prototype, Amodo Design (2026). Original
18. Amodo-Design/Inference-Recomputation-Prototype (GitHub repository), Amodo Design (2026). Original
19. inference-verification: Inference Verification Prototype, Singapore AI Safety Hub (SASH) (2026). Original
20. Internationalising AI Verification, Singapore AI Safety Hub (SASH) (2026). Original
21. Adversarial Entropy Inflation Against Gumbel-Based Inference Verification, N. Kezins (2026). Original
22. Network Tapping for AI Verification: A Technical Assessment, Amodo Design (2026). Original
23. Verifying LLM Inference to Detect Model Weight Exfiltration, R. Rinberg et al. (2025). Original
24. DiFR: Inference Verification Despite Nondeterminism, A. Karvonen et al. (2025). Original
25. TOPLOC: A Locality Sensitive Hashing Scheme for Trustless Verifiable Inference, J. M. Ong et al. (2025). Original
26. PrimeIntellect-ai/toploc (GitHub repository), Prime Intellect (2025). Original
27. INTELLECT-2: A Reasoning Model Trained Through Globally Decentralized Reinforcement Learning, Prime Intellect Team et al. (2025). Original
28. adamkarvonen/difr (GitHub repository), A. Karvonen (2025). Original
29. Scaling Recomputation Inference Verification, Amodo Design (2026). Original
30. AI 2040 Plan A — Verification SITREP, Amodo Design (2026). Original
31. SYNTHETIC-2 Release: Four Million Collaboratively Generated Reasoning Traces, Prime Intellect (2025). Original
32. An Inference Verification Prototype — Stage 1, Amodo Design (2026). Original
33. Bit-Exact AI Inference Verification Without Performance Tradeoffs, N. Cankaya (2026). Original
34. Example Schemes for Verifying High-Stakes AI Agreements, Amodo Design (2026). Original
35. Network Traffic Hashing, Amodo Design (2026). Original