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Tamper evidence for verifier devices and Sampled inference recomputation

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.

• Tamper evidence for verifier devices: 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. ^{12 13 14}

    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. ^{13 15}

    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. ^{16 17}

    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. ¹⁷

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

Open significant failures

2 failures in 1 mechanism

• Seals are often defeated with simple methods in Tamper evidence for verifier devices

  Published defeats of general security seals. They warn about proposed verifier-device seals, but do not demonstrate defeat of an AI verification enclosure or sensor.

  In 1996 a Los Alamos vulnerability assessment defeated all 94 security seals it examined, with 132 defeats in total, using rapid, inexpensive, low-tech methods. It found that seal cost did not predict security. In 2001 Johnston reported that high-tech seals are often easier to defeat than low-tech ones. ^{8 9}

  Known failure · Demonstrated attack · Significant · Open · Mechanism-class evidence. On the record

• Attack classes outside published models in Tamper evidence for verifier devices

  The radio compensation result is emulated using measured channel data under a known-reference attacker model. It is not a physical bypass demonstration against an AI verifier enclosure.

  The authors of the batteryless cover say they cannot assess chemical-solvent attacks, which exceed their expertise, and deem cover removal impractical. Anti-Tamper Radio's reference can drift as the environment or measurement system ages; the authors suggest gradually renewing the reference. A 2025 follow-up by some of the same authors shows, by emulation on measured channel data, that an attacker who knows the reference channel and the needle's effect on it could inject a signal that cancels the change caused by a needle insertion. It proposes a reconfigurable intelligent surface that randomizes the channel as a countermeasure. ^{1 2 10}

  Known failure · Open question · Significant · Open · Mechanism-class evidence. On the record

Scope limitations

• Security depends on inspection protocols in Tamper evidence for verifier devices

  An inspection and protocol requirement drawn from safeguards and enclosure studies, not a reported break of a deployed AI verifier.

  Johnston argues that a seal is no better than the protocols for using it, and that inspectors are usually given little useful information on how to detect tampering. The Sandia survey notes that larger enclosures are hard to inspect fully and that sensor data must be authenticated. ^{6 9}

  Scope limitation · Theoretical argument · Mechanism-class evidence. On the record

• 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. ⁷

  Scope limitation · Theoretical argument. On the record

Open questions

• 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. ⁷

  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. ⁷

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

Not yet demonstrated

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 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

10 blockers recorded

• Tamper evidence for verifier devices
  • No tamper-evident enclosure has been designed for AI verifier hardware at retrofit scale. Hardware trust ⁷
  • Battery-backed designs add bulk, limit operating temperature (+10 °C to +35 °C for the IBM 4765) and complicate transport. Performance & compatibility ²
  • Active monitoring needs power, and visual inspection of large enclosures faces access limits. Access & governance ⁶
  • No evaluation has been published in the AI verification setting. Adversarial validation ⁷
• 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 ⁷
  • 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 ⁷
  • 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 ⁷
  • 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 ⁷
  • 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 ⁷
  • The threat model is under-developed and needs input from cybersecurity and AI threat-modelling experts. Adversarial validation ⁷

From the family or selected implementation's record. ?

Model weights

Not involved: Tamper evidence for verifier devices.

Unspecified for Sampled inference recomputation. Check the implementation record.

Inputs and outputs

Not involved: Tamper evidence for verifier devices.

Unspecified for Sampled inference recomputation. Check the implementation record.

Training data

Not involved: Tamper evidence for verifier devices.

Unspecified for Sampled inference recomputation. Check the implementation record.

Tamper evidence for verifier devices

• Proposed AI 2040 inference-only verification stack Proposed architecture, AI Futures Project

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. Anti-Tamper Radio: System-Level Tamper Detection for Computing Systems, P. Staat et al. (2022). Original
2. Secure Physical Enclosures from Covers with Tamper-Resistance, V. Immler et al. (2019). Original
3. ImpedanceVerif: On-Chip Impedance Sensing for System-Level Tampering Detection, T. Mosavirik et al. (2023). Original
4. IBM 4765 Cryptographic Coprocessor Security Module: Security Policy, IBM Corporation (2012). Original
5. PHYSEC SEAL: Change detection for maximum safety, PHYSEC GmbH (2026). Original
6. Tamper-Indicating Enclosures, A Current Survey, H. A. Smartt & Z. N. Gastelum (2015). Original
7. A System Overview for Near-Term, Low-Trust AI Compute Verification, N. Cankaya (2026). Original
8. Physical Security and Tamper-Indicating Devices, R. G. Johnston & A. R. E. Garcia (1996). Original
9. Tamper Detection for Safeguards and Treaty Monitoring: Fantasies, Realities, and Potentials, R. G. Johnston (2001). Original
10. Anti-Tamper Radio Meets Reconfigurable Intelligent Surface for System-Level Tamper Detection, M. S. Tabar et al. (2025). Original
11. Fingerprinting All AI Cluster I/O Without Mutually Trusted Processors, N. Cankaya et al. (2026). Original
12. Bit-Exact AI Inference Verification Without Performance Tradeoffs, N. Cankaya (2026). Original
13. Verifying LLM Inference to Detect Model Weight Exfiltration, R. Rinberg et al. (2025). Original
14. Adversarial Entropy Inflation Against Gumbel-Based Inference Verification, N. Kezins (2026). Original
15. Example Schemes for Verifying High-Stakes AI Agreements, Amodo Design (2026). Original
16. TOPLOC: A Locality Sensitive Hashing Scheme for Trustless Verifiable Inference, J. M. Ong et al. (2025). Original
17. DiFR: Inference Verification Despite Nondeterminism, A. Karvonen et al. (2025). Original