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Remote detection of data centres and Sampled inference recomputation

No new hardware needed

Remote detection of data centres

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
• Sampled inference recomputation, Low-trust AI compute verification system overview: Analysis

Family findings

• Sampled inference recomputation

  Context for Low-trust AI compute verification system overview. 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.

  • 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. ^{8 9 10}

    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

  • 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. ^{9 11}

    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. 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. ^{12 13}

    Known failure · 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. ¹³

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

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

• Mismatches cannot be attributed to cheating or error in Low-trust AI compute verification system overview

  A failed hash or replay does not show whether it came from an evasion attempt, a random bit flip or an evaluation error. The author notes that if detected anomalies can plausibly be waved off as malfunctions, deterrence becomes less effective, so the parties need an agreed escalation procedure that ends in attribution. ⁶

  Scope limitation · Theoretical argument. On the record

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

• Inspector agents may be manipulable in Low-trust AI compute verification system overview

  Automated compliance screening with LLM-based inspector agents must resist prompt-injection attacks. Adversarially trained systems might hide malicious actions with steganography, which makes backdoor detection an open problem. ⁶

  Open question · Open question. On the record

Open minor failures

1 mechanism with minor failures

• Deliberate faults leak a bit each in Low-trust AI compute verification system overview

  A malicious device can leak one bit by deliberately outputting a wrong result, which blocks a disclosure when the cross-comparison fails. The design therefore needs a pre-agreed budget of tolerated faults. ⁶

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

Not yet demonstrated

Proposed Remote detection of data centres and Proposed Sampled inference recomputation

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

• Operational use Deterministic and bit-exact inference Add
  • Sampled inference recomputation waits on it. Exact replay needs complete hardware and software metadata, and the tolerable slowdown from emulation is an open question.
• Research demo Tamper evidence for verifier devices Add
  • Sampled inference recomputation waits on it. Tamper-evident, rapidly mass-manufacturable and retrofittable enclosures for side-channel defence are an open research question, and physical security against covert communication in every monitored data centre is challenging.
• Research demo Timed challenge-response and memory-occupation challenges Add
  • Sampled inference recomputation waits on it. Distinguishing one server's DRAM contents from another's by challenge-response timing, and a general challenge-response protocol for diverse data types, are open.
• Proposed Network taps and certifiers Add
  • Sampled inference recomputation waits on it. Empirical feasibility of passive optical splitting at 53–112 GBaud under realistic conditions is an open question.
• Proposed Side-channel suppression for isolated facilities Add
  • Sampled inference recomputation waits on it. A mass-manufacturable, good-enough side-channel defence, particularly power-line filtering, has not been constructed or red-teamed.

Missing prerequisites

• Proposed Network taps and certifiers needed by Sampled inference recomputation Add
• Operational use Deterministic and bit-exact inference needed by Sampled inference recomputation Add

Blockers

9 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
• Sampled inference recomputation
  • Empirical feasibility of passive optical splitting at 53–112 GBaud under realistic conditions is an open question. Performance & compatibility. Waits on Network taps and certifiers ⁶
  • Exact replay needs complete hardware and software metadata, and the tolerable slowdown from emulation is an open question. Performance & compatibility. Waits on Deterministic and bit-exact inference ⁶
  • Tamper-evident, rapidly mass-manufacturable and retrofittable enclosures for side-channel defence are an open research question, and physical security against covert communication in every monitored data centre is challenging. Hardware trust. Waits on Tamper evidence for verifier devices ⁶
  • A mass-manufacturable, good-enough side-channel defence, particularly power-line filtering, has not been constructed or red-teamed. Coverage & hidden compute. Waits on Side-channel suppression for isolated facilities ⁶
  • Distinguishing one server's DRAM contents from another's by challenge-response timing, and a general challenge-response protocol for diverse data types, are open. Coverage & hidden compute. Waits on Timed challenge-response and memory-occupation challenges ⁶
  • The threat model is under-developed and needs input from cybersecurity and AI threat-modelling experts. Adversarial validation ⁶

From the family or selected implementation's record. ?

Model weights

Not involved: Remote detection of data centres.

Unspecified for Sampled inference recomputation. Check the implementation record.

Inputs and outputs

Not involved: Remote detection of data centres.

Unspecified for Sampled inference recomputation. Check the implementation record.

Training data

Not involved: Remote detection of data centres.

Unspecified for Sampled inference recomputation. Check the implementation record.

Remote detection of data centres

None on the map

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. 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. A System Overview for Near-Term, Low-Trust AI Compute Verification, N. Cankaya (2026). Original
7. Fingerprinting All AI Cluster I/O Without Mutually Trusted Processors, N. Cankaya et al. (2026). Original
8. Bit-Exact AI Inference Verification Without Performance Tradeoffs, N. Cankaya (2026). Original
9. Verifying LLM Inference to Detect Model Weight Exfiltration, R. Rinberg et al. (2025). Original
10. Adversarial Entropy Inflation Against Gumbel-Based Inference Verification, N. Kezins (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
13. DiFR: Inference Verification Despite Nondeterminism, A. Karvonen et al. (2025). Original