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Excluded by your filters: Side-channel suppression for isolated facilities 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.

• Side-channel suppression for isolated facilities: Analysis · excluded by filters
• Sampled inference recomputation, DiFR (Divergence From Reference): Analysis · excluded by filters

Excluded by your filters

• Side-channel suppression for isolated facilities Attack testing is analysis; the filter asks for at least red-teamed.
• Sampled inference recomputation Attack testing is analysis; the filter asks for at least red-teamed.

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. ^{3 8 10}

    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. ^{3 11}

    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. 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. ^{2 12}

    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. ²

    Open question · Minor · Open. On the record

Open significant flaws

5 flaws in 2 mechanisms

• Supply-chain implants may evade inspection in Side-channel suppression for isolated facilities

  Cankaya identifies malicious hardware embedded deep in purchased components as a residual risk that visual inspection and disassembly may not catch. He notes that radiographic examination under high-security standards could mitigate it. ¹

  Theoretical argument · Significant · Open. On the record

• Openings for airflow, power and optics weaken shielding in Side-channel suppression for isolated facilities

  Cankaya notes that keeping attenuation high while passing high-power airflow, cabling and optical links adds complexity beyond existing shielded-enclosure specifications. ¹

  Theoretical argument · Significant · Open. On the record

• Inspection assumptions may not hold in Side-channel suppression for isolated facilities

  The design's statistical argument assumes that visual or disassembly inspection catches every flaw that is present in a sampled unit. Cankaya is unsure whether destructive teardowns are defence-dominant or offence-dominant. ¹

  Open question · Significant · Open. On the record

• 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. ^{3 8 10}

  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. ²

  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. ²

  Open question · Minor · Open. On the record

Not yet demonstrated

R1 Side-channel suppression for isolated facilities

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

The records connect no other mechanism to this proposal's gaps, open flaws or dependencies.

Blockers

5 blockers recorded

• Side-channel suppression for isolated facilities
  • No prototype or red-team exists; the design is a first-pass viability study. Adversarial validation ¹
  • Volume costs of TEMPEST-grade power-line filters are uncertain, because existing products are mostly made to order. Performance & compatibility 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 ²
  • 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 ^{2 5}
  • 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 ^{7 8}

From the family or selected implementation's record. ?

Model weights

Not involved: Side-channel suppression for isolated facilities.

Unspecified for Sampled inference recomputation. Check the implementation record.

Inputs and outputs

Not involved: Side-channel suppression for isolated facilities.

Unspecified for Sampled inference recomputation. Check the implementation record.

Training data

Not involved: Side-channel suppression for isolated facilities.

Unspecified for Sampled inference recomputation. Check the implementation record.

Side-channel suppression for isolated facilities

• 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 RAND secure inference data center (SIDC) design Proposed architecture, RAND

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. Suppressing Side Channels in an Untrusted Data Center via Retrofitted Defenses, N. Cankaya (2026). Original
2. DiFR: Inference Verification Despite Nondeterminism, A. Karvonen et al. (2025). Original
3. Verifying LLM Inference to Detect Model Weight Exfiltration, R. Rinberg et al. (2025). Original
4. adamkarvonen/difr (GitHub repository), A. Karvonen (2025). Original
5. Scaling Recomputation Inference Verification, Amodo Design (2026). Original
6. Amodo-Design/Inference-Recomputation-Prototype (GitHub repository), Amodo Design (2026). Original
7. AI 2040 Plan A — Verification SITREP, Amodo Design (2026). Original
8. Adversarial Entropy Inflation Against Gumbel-Based Inference Verification, N. Kezins (2026). Original
9. An Inference Verification Prototype — Stage 1, Amodo Design (2026). Original
10. Bit-Exact AI Inference Verification Without Performance Tradeoffs, N. Cankaya (2026). Original
11. Example Schemes for Verifying High-Stakes AI Agreements, Amodo Design (2026). Original
12. TOPLOC: A Locality Sensitive Hashing Scheme for Trustless Verifiable Inference, J. M. Ong et al. (2025). Original