Proposal Explorer

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Excluded by your filters: Network taps and certifiers and Sampled inference recomputation

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

• Network taps and certifiers: Analysis · excluded by filters
• Sampled inference recomputation, DiFR (Divergence From Reference): Analysis · excluded by filters

Excluded by your filters

• Network taps and certifiers Readiness R1 is below the minimum of R4.
• Sampled inference recomputation Readiness R2 is below the minimum of R4.

Family findings

• Sampled inference recomputation

  Context for DiFR (Divergence From Reference). These findings concern the family or other implementations; applicability must be checked against their stated scope.

  • 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. ^{9 12 17}

    Demonstrated attack · Significant · Open. On the record

    Related mechanism R3 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 flaw 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. ^{12 18}

    Theoretical argument · Significant · Open. On the record

    Related mechanism R1 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 flaw is mitigated. In the proposal.

  • 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. ^{11 19}

    Theoretical argument · Significant · Open. On the record

  • 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. ¹¹

    Open question · Minor · Open. On the record

Open significant flaws

7 flaws 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. ^{1 9}

  Theoretical argument · Significant · Open. On the record

  Related mechanism R3 Deterministic and bit-exact inference: Deterministic replay is one of the two remedies the flaw's source names. A pointer, not evidence that this flaw is mitigated. Add

  Related mechanism R2 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 flaw is mitigated. Add

• 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. ^{2 10}

  Open question · Significant · Open. 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. ²

  Open question · Significant · Open. On the record

• 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. ⁵

  Theoretical argument · Significant · Open. 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. ¹

  Open question · Significant · Open. On the record

  Related mechanism R1 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 flaw is mitigated. Add

• Statistical tolerance leaves a covert channel in DiFR (Divergence From Reference)

  Statistical schemes can put an upper bound on an adversary's covert bandwidth, but cannot close it. In the companion exfiltration study, the detector cut exfiltratable information to under 0.5% under benign prompt traffic. It did not cut it to zero. Its authors called the channel's size under adversarial prompts an open empirical question. An independent study attacked that detector, which uses the same Gumbel-margin statistic. An adversary who controls the prompts roughly doubled the bits leaked per token. Across six models, this cut the slowdown from 146–254 times under benign prompts to 60–118 times. The attack widens the exfiltration bound. It does not target DiFR's check that outputs match the declared configuration. ^{9 12 17}

  Demonstrated attack · Significant · Open. On the record

• Speculative decoding and multi-model sampling not evaluated in DiFR (Divergence From Reference)

  The algorithms and experiments cover sampling from a single LLM. Speculative decoding was not evaluated. The authors sketch an extension to one speculative-decoding algorithm, without experiments. They note that other variants would need modified verification and extra metadata. ¹¹

  Open question · Significant · Open. On the record

Open minor flaws

1 mechanism with minor findings

• Mixed hardware widens the honest baseline in DiFR (Divergence From Reference)

  For Qwen3-30B-A3B, pooling honest runs across A100 and H200 GPUs and parallelism setups broadened the honest score distribution. Token-DiFR then failed 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 did. The authors report that matched provider and verifier environments, or pooling that weights rare large deviations, restore detection. ¹¹

  Open question · Minor · Open. On the record

Not yet demonstrated

R1 Network taps and certifiers

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

• R3 Deterministic and bit-exact inference Add

  Excluded by filters: readiness R3

  • Bears on the open significant flaw “Output nondeterminism leaves covert capacity” in Network taps and certifiers. Deterministic replay is one of the two remedies the flaw's source names.
  • Network taps and certifiers waits on it. Nondeterministic inference leaves covert capacity in outputs that hashing cannot remove.
• R2 Bounding unexplained information in outputs Add

  Excluded by filters: readiness R2

  • Bears on the open significant flaw “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.
• R1 Side-channel suppression for isolated facilities Add

  Excluded by filters: readiness R1

  • Bears on the open significant flaw “Completeness rests on physical monitoring left out of scope” in Network taps and certifiers. Addresses the radio, power-line and thermal channels that network-level designs leave out.
  • Network taps and certifiers waits on it. Radio, power-line and thermal channels are not addressed by network-level designs.
• R2 Tamper evidence for verifier devices Add

  Excluded by filters: readiness R2

  • Network taps and certifiers waits on it. Taps and gateway devices need tamper-evident housing and physical monitoring so that traffic cannot bypass them.

Missing prerequisites

• R3 Deterministic and bit-exact inference needed by Network taps and certifiers Add
• R2 Tamper evidence for verifier devices needed by Network taps and certifiers Add
• R1 Side-channel suppression for isolated facilities needed by Network taps and certifiers Add

Blockers

8 blockers recorded

• 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 ^{10 20}
  • Nondeterministic inference leaves covert capacity in outputs that hashing cannot remove. Evidence binding. Waits on Deterministic and bit-exact inference ¹
  • 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 ^{1 3}
  • Radio, power-line and thermal channels are not addressed by network-level designs. Coverage & hidden compute. Waits on Side-channel suppression for isolated facilities ¹
  • Red-teaming by specialists is called for but has not been reported. Adversarial validation ¹
• Sampled inference recomputation
  • The verifier needs the model weights, so outsiders cannot use the method to verify providers of closed-weights models. Privacy & leakage ¹¹
  • The verifier must know and match the provider's sampling procedure, and in one prototype a sampling mismatch in a newer vLLM version produced large spurious logit differences. Performance & compatibility ^{11 14}
  • No independent red-team of DiFR's consistency check has been published, Amodo rates recomputation red-teaming 'not started', and the one independent attack study targets an exfiltration detector built on the same statistic. Adversarial validation ^{9 15}

From the family or selected implementation's record. ?

Model weights

Depends on the design for Network taps and certifiers.

Unspecified for Sampled inference recomputation. Check the implementation record.

Inputs and outputs

Depends on the design for Network taps and certifiers.

Unspecified for Sampled inference recomputation. Check the implementation record.

Training data

Depends on the design for Network taps and certifiers.

Unspecified for Sampled inference recomputation. Check the implementation record.

Network taps and certifiers

• R1 AI 2040 inference-only verification stack Proposed architecture, AI Futures Project
• R1 Low-trust AI compute verification system overview Proposed architecture, Machine Intelligence Research Institute
• R1 SASH confidential network logger Research prototype, Singapore AI Safety Hub (SASH)

Sampled inference recomputation

• R1 AI 2040 inference-only verification stack Proposed architecture, AI Futures Project
• R2 DiFR (Divergence From Reference) Research prototype
• R1 Low-trust AI compute verification system overview Proposed architecture, Machine Intelligence Research Institute
• R1 SASH confidential network logger Research prototype, Singapore AI Safety Hub (SASH)
• R3 TOPLOC Open-source project, Prime Intellect

1. Fingerprinting All AI Cluster I/O Without Mutually Trusted Processors, N. Cankaya et al. (2026). Original
2. The Fundamentals and Feasibility of Secure Network Taps for Verifying AI Datacenter Use, N. Cankaya (2026). Original
3. A System Overview for Near-Term, Low-Trust AI Compute Verification, N. Cankaya (2026). Original
4. Verification Plan, R. Dean (2026). Original
5. Fitting a Network TAP to our Inference Verification Prototype, Amodo Design (2026). Original
6. Amodo-Design/Inference-Recomputation-Prototype (GitHub repository), Amodo Design (2026). Original
7. inference-verification: Inference Verification Prototype, Singapore AI Safety Hub (SASH) (2026). Original
8. Internationalising AI Verification, Singapore AI Safety Hub (SASH) (2026). Original
9. Adversarial Entropy Inflation Against Gumbel-Based Inference Verification, N. Kezins (2026). Original
10. Network Tapping for AI Verification: A Technical Assessment, Amodo Design (2026). Original
11. DiFR: Inference Verification Despite Nondeterminism, A. Karvonen et al. (2025). Original
12. Verifying LLM Inference to Detect Model Weight Exfiltration, R. Rinberg et al. (2025). Original
13. adamkarvonen/difr (GitHub repository), A. Karvonen (2025). Original
14. Scaling Recomputation Inference Verification, Amodo Design (2026). Original
15. AI 2040 Plan A — Verification SITREP, Amodo Design (2026). Original
16. An Inference Verification Prototype — Stage 1, Amodo Design (2026). Original
17. Bit-Exact AI Inference Verification Without Performance Tradeoffs, N. Cankaya (2026). Original
18. Example Schemes for Verifying High-Stakes AI Agreements, Amodo Design (2026). Original
19. TOPLOC: A Locality Sensitive Hashing Scheme for Trustless Verifiable Inference, J. M. Ong et al. (2025). Original
20. Network Traffic Hashing, Amodo Design (2026). Original