Proposal Explorer

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Excluded by your filters: Remote detection of data centres and Bounding unexplained information in outputs

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.

• Remote detection of data centres: Analysis · excluded by filters
• Bounding unexplained information in outputs: Independent red-team · excluded by filters

Excluded by your filters

• Remote detection of data centres Legacy independent-evaluation filter (R4): no matching legacy code.
• Bounding unexplained information in outputs Legacy independent-evaluation filter (R4): no matching legacy code.

Open significant failures

1 failure in 1 mechanism

• Prompt-controlled entropy inflation widens the covert channel in Bounding unexplained information in outputs

  Gumbel-based inference verification tolerates token choices that honest GPU nondeterminism could produce, and the size of that tolerated set grows with the model's output entropy. Kezins, an independent researcher, showed that an adversary who controls the prompt distribution can raise output entropy and roughly double the bits leaked per token. Across six models of 1 to 32 billion parameters, this cut the slowdown from 146–254 times under benign prompts to 60–118 times. Kezins argues that architectures built on the same unexplained-information bound inherit this attack surface, and recommends calibrating tolerances against local token entropy rather than benign traffic. ^{7 8}

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

  Related mechanism Operational use Deterministic and bit-exact inference: Bit-exact replay would remove the tolerance for numerical noise that sets the size of this channel. The record notes that it needs full hardware and software metadata. A pointer, not evidence that this failure is mitigated. Add

Scope limitations

• Facilities can be disguised or hidden in Remote detection of data centres

  Halstead and Larsen discuss two ways to hide a facility. One is to disguise it as a legitimate industrial site. The other is to build it underground, with cooling that avoids visible heat plumes. They note that the underground option requires bespoke engineering. ¹

  Scope limitation · Theoretical argument. On the record

• Small sites may not be detectable in Remote detection of data centres

  Halstead and Larsen conclude that a sufficiently small covert project could not be ruled out with confidence. In their estimates, the chance of detection is lower for smaller sites. Krawec notes that small data centres in existing buildings may lack the distinctive features of large facilities. ^{1 3}

  Scope limitation · Theoretical argument. On the record

  Related mechanism Proposed Chip registries and manufacturing records: Accounts for chips from the fab onwards, which does not depend on a site being visible. A pointer, not evidence that this failure is mitigated. Add

• Information the declared computation explains is not bounded in Bounding unexplained information in outputs

  The bound limits unexplained bits only. Outputs that the declared computation fully explains can still carry valuable information: a compression study notes that an adversary with inference access can extract more proprietary information per bit than naive transmission allows. ^{6 9}

  Scope limitation · Theoretical argument. On the record

• Channels other than checked outputs are outside the bound in Bounding unexplained information in outputs

  The inference-verification scheme treats side channels as out of scope. A low-trust system design argues that suppressing physical covert bandwidth below kilobits per second is much more achievable than aiming for zero, and that a malicious device can leak one bit of information by deliberately outputting a wrong result. ^{7 10}

  Scope limitation · Theoretical argument. On the record

  Related mechanism Proposed Side-channel suppression for isolated facilities: Physical side channels need separate suppression, which is this mechanism's purpose. A pointer, not evidence that this failure is mitigated. Add

Open questions

• Search for unknown sites is undemonstrated in Remote detection of data centres

  Krawec reports that telling data centres apart from other industrial facilities systematically is difficult. Automating detection would need large amounts of training imagery and a purpose-trained model. In Krawec's words, automated data-centre detection "remains primarily conceptual at present". ³

  Open question · Open question. On the record

• The facility-level design is untested in Bounding unexplained information in outputs

  The compute-verification architecture is described with protocol details, potential attacks and prototyping plans, but no prototype results have been published. ⁶

  Open question · Open question. On the record

Not yet demonstrated

Proposed Remote detection of data centres

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

• Operational use Deterministic and bit-exact inference Add

  Excluded by filters: legacy evaluation filter

  • Bears on the open significant failure “Prompt-controlled entropy inflation widens the covert channel” in Bounding unexplained information in outputs. Bit-exact replay would remove the tolerance for numerical noise that sets the size of this channel. The record notes that it needs full hardware and software metadata.
  • Bounding unexplained information in outputs waits on it. Tolerance for numerical nondeterminism sets the size of the residual channel; bit-exact replay would remove it but needs full hardware and software metadata.
• Research demo Bandwidth limits and compartmentalization Add

  Excluded by filters: legacy evaluation filter

  • Bounding unexplained information in outputs waits on it. The prover's compute must be isolated so that all traffic passes through the verifier's interlock; any unmonitored path voids the bound.
• Proposed Side-channel suppression for isolated facilities Add

  Excluded by filters: legacy evaluation filter

  • Bounding unexplained information in outputs waits on it. Physical side channels need separate suppression, and one design treats a low residual bandwidth, rather than zero, as the realistic target.
• Operational use Sampled inference recomputation Add

  Excluded by filters: legacy evaluation filter

  • Bounding unexplained information in outputs depends on it.
• Proposed Network taps and certifiers Add

  Excluded by filters: legacy evaluation filter

  • Bounding unexplained information in outputs depends on it.

Missing prerequisites

• Operational use Sampled inference recomputation needed by Bounding unexplained information in outputs Add
• Research demo Bandwidth limits and compartmentalization needed by Bounding unexplained information in outputs Add
• Proposed Side-channel suppression for isolated facilities needed by Bounding unexplained information in outputs Add
• Proposed Network taps and certifiers needed by Bounding unexplained information in outputs Add

Blockers

8 blockers recorded

• Remote detection of data centres
  • Wide-area, automated detection of data centres is not yet practical and needs large training datasets. Coverage & hidden compute ³
  • No measured detection or false-alarm rates for finding undeclared facilities have been published. Adversarial validation ^{1 3}
  • Recent high-resolution imagery is costly, is limited by weather and needs trained analysts. Access & governance ³
• Bounding unexplained information in outputs
  • The prover's compute must be isolated so that all traffic passes through the verifier's interlock; any unmonitored path voids the bound. Coverage & hidden compute. Waits on Bandwidth limits and compartmentalization ⁶
  • Physical side channels need separate suppression, and one design treats a low residual bandwidth, rather than zero, as the realistic target. Coverage & hidden compute. Waits on Side-channel suppression for isolated facilities ^{7 10}
  • Tolerance for numerical nondeterminism sets the size of the residual channel; bit-exact replay would remove it but needs full hardware and software metadata. Protocol soundness. Waits on Deterministic and bit-exact inference ^{8 10}
  • Recomputation over confidential weights and inputs needs a protected setting: prover recomputation in a verifier-controlled enclosure, verifier recomputation in a prover-controlled enclosure, or zero-knowledge proofs. Privacy & leakage ⁶
  • No prototype of the facility-level architecture exists to red-team. Adversarial validation ⁶

From the family or selected implementation's record. ?

Model weights

Depends on the design for Bounding unexplained information in outputs.

Not involved: Remote detection of data centres.

Inputs and outputs

Depends on the design for Bounding unexplained information in outputs.

Not involved: Remote detection of data centres.

Training data

Not involved: Remote detection of data centres and Bounding unexplained information in outputs.

Remote detection of data centres

None on the map

Bounding unexplained information in outputs

None on the map

1. Covert AI Projects, B. Halstead & T. Larsen (2026). Original
2. Verifying International Agreements on AI: Six Layers of Verification for Rules on Large-Scale AI Development and Deployment, M. Baker et al. (2025). Original
3. Tracking Hyperscale AI Data Center Growth with Satellite Imagery, C. Krawec (2026). Original
4. Introducing the Frontier Data Centers Hub, Epoch AI (2025). Original
5. AI Data Centers Documentation – Methodology, Epoch AI (2026). Original
6. Verifying AI Compute by Bounding Unexplained Information Exfiltration, J. Petrie & Y. Mühlhäuser (2026). Original
7. Verifying LLM Inference to Detect Model Weight Exfiltration, R. Rinberg et al. (2025). Original
8. Adversarial Entropy Inflation Against Gumbel-Based Inference Verification, N. Kezins (2026). Original
9. Haiku to Opus in Just 10 bits: LLMs Unlock Large Compression Gains, R. Rinberg et al. (2026). Original
10. A System Overview for Near-Term, Low-Trust AI Compute Verification, N. Cankaya (2026). Original