Proposal Explorer

Add claims to see which ones the mechanisms address.

Built for an adversarial prover

Chip location verification, Tamper evidence for verifier devices and Remote detection of data centres

No new hardware needed

Chip location verification and Remote detection of data centres

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

• Chip location verification: Analysis
• Tamper evidence for verifier devices: Analysis
• Remote detection of data centres: Analysis

Open significant flaws

10 flaws in 3 mechanisms

• Extracting a chip's key lets another device answer for it in Chip location verification

  Ping-based protocols rely on cryptographic keys stored on the chip. Tee and Happel argue that an adversary with physical access could extract these keys and so compromise location verification. They propose GPU fingerprints as a mitigation, so far tested on 24 GPUs. Brass and Aarne assume the keys are stored securely, for example in a TPM. ^{1 6}

  Theoretical argument · Significant · Open. On the record

• Added delay can shift an estimated position in Chip location verification

  Brass and Aarne cite internet-geolocation research in which artificially increased round-trip times moved the estimated location by up to 1,000 km, with a 74% chance of avoiding detection. Avellar and Grunewald list inflated ping times from circuitous routing as an evasion route. Added delay only loosens a distance bound, and Brass and Aarne propose a hard time limit as the counter: a chip that replies too slowly cannot be ruled out of a restricted location. ^{1 4}

  Demonstrated attack · Significant · Open. On the record

• Faster-than-assumed network paths in Chip location verification

  Brass and Aarne list dark fibre and other private high-speed interconnects as ways to lower measured delays artificially. They judge that leasing dark fibre would probably not be a considerable challenge for covertly or openly adversarial actors. Avellar and Grunewald note that this can make a chip appear to be somewhere else entirely. A limit set at the vacuum speed of light cannot be beaten, but it makes honest chips fail more often. ^{1 4}

  Theoretical argument · Significant · Open. On the record

• Compromised landmarks can falsify measurements in Chip location verification

  A party that controls landmark servers can report false timing. Brass and Aarne cite research in which manipulating a third of the landmarks shifted the estimated location by about 700 km. Avellar and Grunewald note that compromised landmarks let adversaries spoof travel-time measurements directly. The draft specification asks verifiers to require anchors in diverse places, run by several independent operators. ^{1 4 5}

  Theoretical argument · Significant · Open. On the record

• 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. ^{15 16}

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

• 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. ^{13 16}

  Theoretical argument · 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. ^{8 9 17}

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

• 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. ¹⁸

  Theoretical argument · Significant · Open. 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. ^{18 20}

  Theoretical argument · Significant · Open. On the record

  Related mechanism R1 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 flaw is mitigated. Add

• 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". ²⁰

  Open question · Significant · Open. On the record

Not yet demonstrated

R1 Chip location verification and R1 Remote detection of data centres

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

• R1 Chip registries and manufacturing records Add
  • Bears on the open significant flaw “Small sites may not be detectable” in Remote detection of data centres. Accounts for chips from the fab onwards, which does not depend on a site being visible.
• R3 TEE remote attestation for AI workloads Add
  • Chip location verification depends on it.

Missing prerequisites

• R3 TEE remote attestation for AI workloads needed by Chip location verification Add

Blockers

11 blockers recorded

• Chip location verification
  • No public code or reproducible end-to-end location results are available for the reported H100 prototype. Adversarial validation ^{2 3}
  • Per-chip keys must be provisioned and protected against extraction; hardware-integrated, tamper-resistant versions still need R&D. Hardware trust ^{1 6 23}
  • The time limit forces a trade-off: a limit at the speed of light in fibre can be beaten by faster links, while one at the vacuum speed of light makes honest chips fail often. Protocol soundness ¹
  • A trusted landmark network must be built and secured, and who should operate it, under what oversight, is unsettled. Access & governance ^{1 4}
• Tamper evidence for verifier devices
  • No tamper-evident enclosure has been designed for AI verifier hardware at retrofit scale. Hardware trust ¹⁴
  • Battery-backed designs add bulk, limit operating temperature (+10 °C to +35 °C for the IBM 4765) and complicate transport. Performance & compatibility ⁹
  • Active monitoring needs power, and visual inspection of large enclosures faces access limits. Access & governance ¹³
  • No evaluation has been published in the AI verification setting. Adversarial validation ¹⁴
• Remote detection of data centres
  • Wide-area, automated detection of data centres is not yet practical and needs large training datasets. Coverage & hidden compute ²⁰
  • No measured detection or false-alarm rates for finding undeclared facilities have been published. Adversarial validation ^{18 20}
  • Recent high-resolution imagery is costly, is limited by weather and needs trained analysts. Access & governance ²⁰

From the family or selected implementation's record. ?

Model weights

Not involved: Chip location verification, Tamper evidence for verifier devices and Remote detection of data centres.

Inputs and outputs

Not involved: Chip location verification, Tamper evidence for verifier devices and Remote detection of data centres.

Training data

Not involved: Chip location verification, Tamper evidence for verifier devices and Remote detection of data centres.

Chip location verification

• R1 Lucid sovereignty (location) certificates Standard, Lucid Computing

Tamper evidence for verifier devices

• R1 AI 2040 inference-only verification stack Proposed architecture, AI Futures Project

Remote detection of data centres

None on the map

1. Location Verification for AI Chips, A. Brass & O. Aarne (2024). Original
2. Location Verification for AI Chips (issue brief), A. Brass (2025). Original
3. Ping-based Location, Ulyssean (2025). Original
4. Near-Term Verification Methods for AI Chip Exports, B. Avellar & E. Grunewald (2026). Original
5. Sovereignty Certificates: draft specification, version 0.1.0, Sovereignty Certificates Working Group (2025). Original
6. GPU Fingerprinting for Location Verification, W. Tee & J. Happel (2026). Original
7. Secure, Governable Chips: Using On-Chip Mechanisms to Manage National Security Risks from AI & Advanced Computing, O. Aarne et al. (2024). Original
8. Anti-Tamper Radio: System-Level Tamper Detection for Computing Systems, P. Staat et al. (2022). Original
9. Secure Physical Enclosures from Covers with Tamper-Resistance, V. Immler et al. (2019). Original
10. ImpedanceVerif: On-Chip Impedance Sensing for System-Level Tampering Detection, T. Mosavirik et al. (2023). Original
11. IBM 4765 Cryptographic Coprocessor Security Module: Security Policy, IBM Corporation (2012). Original
12. PHYSEC SEAL: Change detection for maximum safety, PHYSEC GmbH (2026). Original
13. Tamper-Indicating Enclosures, A Current Survey, H. A. Smartt & Z. N. Gastelum (2015). Original
14. A System Overview for Near-Term, Low-Trust AI Compute Verification, N. Cankaya (2026). Original
15. Physical Security and Tamper-Indicating Devices, R. G. Johnston & A. R. E. Garcia (1996). Original
16. Tamper Detection for Safeguards and Treaty Monitoring: Fantasies, Realities, and Potentials, R. G. Johnston (2001). Original
17. Anti-Tamper Radio Meets Reconfigurable Intelligent Surface for System-Level Tamper Detection, M. S. Tabar et al. (2025). Original
18. Covert AI Projects, B. Halstead & T. Larsen (2026). Original
19. Verifying International Agreements on AI: Six Layers of Verification for Rules on Large-Scale AI Development and Deployment, M. Baker et al. (2025). Original
20. Tracking Hyperscale AI Data Center Growth with Satellite Imagery, C. Krawec (2026). Original
21. Introducing the Frontier Data Centers Hub, Epoch AI (2025). Original
22. AI Data Centers Documentation – Methodology, Epoch AI (2026). Original
23. Hardware-Level Governance of AI Compute: A Feasibility Taxonomy for Regulatory Compliance and Treaty Verification, S. Ansari (2026). Original