Proposal Explorer

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Chip location verification

No new hardware needed

Chip location verification and On-chip telemetry from timing, memory and performance counters

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
• On-chip telemetry from timing, memory and performance counters: Red-teamed

Open critical flaws

• Software-read telemetry can be forged by the operator in On-chip telemetry from timing, memory and performance counters

  NVML-based classification assumes trustworthy telemetry. Without a tamper-resistant read path, an authenticated telemetry channel and secure boot of the monitoring software, an operator who controls the full software stack could forge counter values. Monfared et al. start from the same premise: current GPUs expose little trusted telemetry and can be modified or virtualized. ^{8 10}

  Theoretical argument · Critical · Open. On the record

  Related mechanism R1 Hardware-enabled guarantees (flexHEG) and guarantee processors: A guarantee processor on the chip would give the tamper-resistant, authenticated telemetry path the flaw says is missing. A pointer, not evidence that this flaw is mitigated. Add

Open significant flaws

7 flaws in 2 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

• Adversarially disguised fine-tuning partly evades classification in On-chip telemetry from timing, memory and performance counters

  Across 20 evasion strategy families in five monitor-evader rounds, the classifier's accuracy against the most challenging disguised workloads held out from training was 43–87%. White-box LoRA fine-tuning was the only evasion family detected less than half the time. The evaluation covered single nodes, PyTorch-level evasion and NVIDIA hardware. ¹⁰

  Demonstrated attack · Significant · Open. On the record

• Timing challenges do not identify the individual chip in On-chip telemetry from timing, memory and performance counters

  GEMM and VDF challenges can be answered by identical GPUs elsewhere, and floating-point fingerprints distinguish GPU models, not individual devices. GPU virtualization adds timing leakage that prevents attributing compute use. ⁸

  Theoretical argument · Significant · Open. On the record

• Counters leak information about protected workloads in On-chip telemetry from timing, memory and performance counters

  Performance counters have been used as a side channel against TEEs, for example in CounterSEVeillance. NVIDIA disables performance counters in full confidential-computing mode, stating that they could provide an avenue for side-channel attacks. Richer counters for verification therefore pull against confidentiality. ^{11 12}

  Theoretical argument · Significant · Open. On the record

Open minor flaws

1 mechanism with minor findings

• No quantified error rates or formal thresholds for timing primitives in On-chip telemetry from timing, memory and performance counters

  Monfared et al. state that false-positive and false-negative rates are not quantified and leave hardware-specific formal thresholds to future work. ⁸

  Open question · Minor · Open. On the record

Not yet demonstrated

R1 Chip location verification

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

• R1 Hardware-enabled guarantees (flexHEG) and guarantee processors Add
  • Bears on the open critical flaw “Software-read telemetry can be forged by the operator” in On-chip telemetry from timing, memory and performance counters. A guarantee processor on the chip would give the tamper-resistant, authenticated telemetry path the flaw says is missing.
  • On-chip telemetry from timing, memory and performance counters waits on it. Shipping accelerators need a tamper-resistant, authenticated telemetry path.
• R3 TEE remote attestation for AI workloads Add
  • Chip location verification depends on it.
  • On-chip telemetry from timing, memory and performance counters depends on it.

Missing prerequisites

• R3 TEE remote attestation for AI workloads needed by Chip location verification and On-chip telemetry from timing, memory and performance counters Add

Shared foundations

• TEE remote attestation for AI workloads relied on by Chip location verification and On-chip telemetry from timing, memory and performance counters

Blockers

8 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 13}
  • 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}
• On-chip telemetry from timing, memory and performance counters
  • Shipping accelerators need a tamper-resistant, authenticated telemetry path. Hardware trust. Waits on Hardware-enabled guarantees (flexHEG) and guarantee processors ^{9 10}
  • NVIDIA's full confidential-computing mode disables the hardware performance counters its profiling tools use, so telemetry that needs them conflicts with it. Privacy & leakage ^{11 12}
  • Continuous challenge puzzles cost power and throughput on production workloads. Performance & compatibility ⁸
  • Evaluation has not gone beyond single nodes, framework-level evasion and one vendor's hardware. Adversarial validation ¹⁰

From the family or selected implementation's record. ?

Model weights

Depends on the design for On-chip telemetry from timing, memory and performance counters.

Not involved: Chip location verification.

Inputs and outputs

Depends on the design for On-chip telemetry from timing, memory and performance counters.

Not involved: Chip location verification.

Training data

Depends on the design for On-chip telemetry from timing, memory and performance counters.

Not involved: Chip location verification.

Chip location verification

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

On-chip telemetry from timing, memory and performance counters

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. Timing and Memory Telemetry on GPUs for AI Governance, S. K. Monfared et al. (2026). Original
9. Guaranteeable Memory: An HBM-Based Chiplet for Verifiable AI Workloads, J. Petrie (2025). Original
10. Detecting Hidden ML Training With Zero-Overhead Telemetry, R. Rahman & S. Tajdari (2026). Original
11. On TEEs for Privacy-Preserving Monitoring in AI Governance, Gloria Z (2026). Original
12. NVIDIA Secure AI with Blackwell and Hopper GPUs (White Paper), NVIDIA (2025). Original
13. Hardware-Level Governance of AI Compute: A Feasibility Taxonomy for Regulatory Compliance and Treaty Verification, S. Ansari (2026). Original