Proposal Explorer

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Bandwidth limits and compartmentalization and Tamper evidence for verifier devices

Not counted

Excluded by your filters: Whole-workload recomputation (reproducible packets)

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.

• Bandwidth limits and compartmentalization: Analysis
• Tamper evidence for verifier devices: Analysis

Excluded by your filters

• Whole-workload recomputation (reproducible packets) Attack testing is none; the filter asks for at least published analysis.

Open significant failures

4 failures in 2 mechanisms

• Operator control of pod routing collapses the bound in Bandwidth limits and compartmentalization

  Lucid's analysis finds that if the operator can freely assign pods to routers, it could dedicate a whole cell of 100 or more pods to one pipeline stage. The bound then falls to about 90–220x uncompressed and as low as about 25x with compression. The proposed mitigation, auditor-controlled random assignment that is periodically re-randomized, has not been implemented. ⁷

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

• Parallel scale-up switches are hard enforcement points in Bandwidth limits and compartmentalization

  In GB200 topologies, GPUs reach GPUs in other nodes through NVSwitches without a NIC on the path. Amodo notes that limits are hard to enforce there because many switches work in parallel, so compromising one or two would bypass the limit. ¹⁰

  Known failure · 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. ^{19 20}

  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. ^{13 14 21}

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

Scope limitations

• Spare compute is outside the scheme in Whole-workload recomputation (reproducible packets)

  The plan states that it does not verify that spare compute is not used for unapproved workloads, because this seems very challenging. Recomputation checks the correctness of declared work, not its completeness. ^{1 2}

  Scope limitation · Theoretical argument. On the record

  Related mechanism Proposed Proofs of useful work for capacity accounting: Proposed as one input to accounting for spare capacity on declared hardware. A pointer, not evidence that this failure is mitigated. Add

• Undeclared local storage raises per-pod capacity in Bandwidth limits and compartmentalization

  More memory or storage per pod helps an adversary. Lucid requires per-pod storage to be declared, capped and physically inspected. ⁷

  Scope limitation · Theoretical argument. On the record

• Training within one pod is not covered in Bandwidth limits and compartmentalization

  Lucid's bounds concern pre-training models larger than the pods are sized for. Training models that fit in one pod, fine-tuning and reinforcement-learning post-training within one pod are outside the modelled threat. ⁷

  Scope limitation · Open question. 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. ^{18 20}

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

Open questions

• Non-compliant work could be encoded inside compliant-looking packets in Whole-workload recomputation (reproducible packets)

  The plan notes that an AI company might try to encode a non-compliant workload inside a workload that looks compliant on the surface. ¹

  Open question · Theoretical argument. On the record

• Low-communication training reduces the bandwidth training needs in Bandwidth limits and compartmentalization

  DiLoCo matched fully synchronous training on 8 workers while communicating 500 times less. Rahman writes that this family of methods theoretically allows large-scale training with less than 100 Mbps. Lucid includes these methods in its bounds, but notes that extreme activation compression, architectures with unusually small inter-layer widths, or modular paradigms could erode the margin. ^{7 11 12}

  Open question · Theoretical argument. On the record

Not yet demonstrated

Proposed Whole-workload recomputation (reproducible packets)

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

• Operational use Deterministic and bit-exact inference Add
  • Whole-workload recomputation (reproducible packets) waits on it. Workloads are not reproducible by default, and achieving reproducibility may cost performance.
• Proposed Network taps and certifiers Add
  • Whole-workload recomputation (reproducible packets) waits on it. All traffic must reach the recomputation server via network taps, and the server's integrity is critical.
  • Bandwidth limits and compartmentalization waits on it. The verifier must know that all traffic leaving a pod crosses the capped, monitored links.

Missing prerequisites

• Operational use Deterministic and bit-exact inference needed by Whole-workload recomputation (reproducible packets) Add
• Proposed Network taps and certifiers needed by Whole-workload recomputation (reproducible packets) Add

Shared foundations

• Network taps and certifiers relied on by Whole-workload recomputation (reproducible packets) and Bandwidth limits and compartmentalization

Blockers

12 blockers recorded

• Whole-workload recomputation (reproducible packets)
  • Workloads are not reproducible by default, and achieving reproducibility may cost performance. Performance & compatibility. Waits on Deterministic and bit-exact inference ¹
  • Network packets are not individually reproducible by default; making them so may need considerable software, firmware and hardware work. Amodo rates this 'not on track'. Performance & compatibility ⁴
  • All traffic must reach the recomputation server via network taps, and the server's integrity is critical. Hardware trust. Waits on Network taps and certifiers ^{1 4}
  • Recomputing training steps needs checkpoints: writing one at every step would cost more than 100% overhead, so Amodo's design needs a spare data-parallel replica that tracks the weights instead. Performance & compatibility ²
• Bandwidth limits and compartmentalization
  • No cap that a verifier can check has been implemented or red-teamed. Adversarial validation ⁷
  • The verifier must know that all traffic leaving a pod crosses the capped, monitored links. Coverage & hidden compute. Waits on Network taps and certifiers ⁸
  • Shaping devices and routing assignments must be trusted by both parties; Amodo has not yet fully analysed resilience to a compromised DPU. Hardware trust. Waits on Tamper evidence for verifier devices ^{7 10}
  • Advances in low-communication training could shrink the margin that the cap enforces. Capacity bounds ^{7 11 12}
• Tamper evidence for verifier devices
  • No tamper-evident enclosure has been designed for AI verifier hardware at retrofit scale. Hardware trust ⁸
  • Battery-backed designs add bulk, limit operating temperature (+10 °C to +35 °C for the IBM 4765) and complicate transport. Performance & compatibility ¹⁴
  • Active monitoring needs power, and visual inspection of large enclosures faces access limits. Access & governance ¹⁸
  • No evaluation has been published in the AI verification setting. Adversarial validation ⁸

From the family or selected implementation's record. ?

Model weights

Depends on the design for Whole-workload recomputation (reproducible packets).

Not involved: Bandwidth limits and compartmentalization and Tamper evidence for verifier devices.

Inputs and outputs

Depends on the design for Whole-workload recomputation (reproducible packets).

Not involved: Bandwidth limits and compartmentalization and Tamper evidence for verifier devices.

Training data

Depends on the design for Whole-workload recomputation (reproducible packets).

Not involved: Bandwidth limits and compartmentalization and Tamper evidence for verifier devices.

Whole-workload recomputation (reproducible packets)

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

Bandwidth limits and compartmentalization

• Proposed AI 2040 inference-only verification stack Proposed architecture, AI Futures Project
• Proposed RAND secure inference data center (SIDC) design Proposed architecture, RAND

Tamper evidence for verifier devices

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

1. Verification Plan, R. Dean (2026). Original
2. Example Schemes for Verifying High-Stakes AI Agreements, Amodo Design (2026). Original
3. Scaling Recomputation Inference Verification, Amodo Design (2026). Original
4. AI 2040 Plan A — Verification SITREP, Amodo Design (2026). Original
5. Get Involved in Verification, AI Futures Project (2026). Original
6. Proof-of-Learning is Currently More Broken Than You Think, C. Fang et al. (2023). Original
7. Traffic Shaping for Workload Classification, Lucid Computing (2026). Original
8. A System Overview for Near-Term, Low-Trust AI Compute Verification, N. Cankaya (2026). Original
9. De-risking Interconnect Limits for AI Verification, A. Scher et al. (2026). Original
10. The Tray as a Bandwidth Boundary, Amodo Design (2026). Original
11. DiLoCo: Distributed Low-Communication Training of Language Models, A. Douillard et al. (2024). Original
12. Does Distributed Training Undermine Compute Governance?, R. Rahman (2026). Original
13. Anti-Tamper Radio: System-Level Tamper Detection for Computing Systems, P. Staat et al. (2022). Original
14. Secure Physical Enclosures from Covers with Tamper-Resistance, V. Immler et al. (2019). Original
15. ImpedanceVerif: On-Chip Impedance Sensing for System-Level Tampering Detection, T. Mosavirik et al. (2023). Original
16. IBM 4765 Cryptographic Coprocessor Security Module: Security Policy, IBM Corporation (2012). Original
17. PHYSEC SEAL: Change detection for maximum safety, PHYSEC GmbH (2026). Original
18. Tamper-Indicating Enclosures, A Current Survey, H. A. Smartt & Z. N. Gastelum (2015). Original
19. Physical Security and Tamper-Indicating Devices, R. G. Johnston & A. R. E. Garcia (1996). Original
20. Tamper Detection for Safeguards and Treaty Monitoring: Fantasies, Realities, and Potentials, R. G. Johnston (2001). Original
21. Anti-Tamper Radio Meets Reconfigurable Intelligent Surface for System-Level Tamper Detection, M. S. Tabar et al. (2025). Original