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Memory wiping and proofs of secure erasure, Tamper evidence for verifier devices and Bandwidth limits and compartmentalization

No new hardware needed

Memory wiping and proofs of secure erasure

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.

• Memory wiping and proofs of secure erasure: Analysis
• Tamper evidence for verifier devices: Analysis
• Bandwidth limits and compartmentalization: Analysis

Open significant failures

5 failures in 3 mechanisms

• Outside help during challenges in Memory wiping and proofs of secure erasure

  Classic proofs of secure erasure assume the device is isolated during the protocol. Bursuc et al. relax this to a bound on how close a helper can be, enforced by round-trip times. In data centres, remote memory access has round trips of about 1–2 µs, against about 70–200 ns for local DRAM. The MIRI overview therefore says verification depends on ruling out RDMA by latency or physical disconnection. ^{3 4}

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

  Related mechanism Research demo Timed challenge-response and memory-occupation challenges: Timed challenges bound how far away a helper can be by how quickly it must answer. A pointer, not evidence that this failure is mitigated. Add

  Related mechanism Research demo Bandwidth limits and compartmentalization: Removing or capping links between groups of accelerators limits remote memory access during a challenge. A pointer, not evidence that this failure is mitigated. In the proposal.

• 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}

  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. ^{9 10 17}

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

• 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. ¹⁸

  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. ²⁰

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

Scope limitations

• Memory the wipe cannot reach in Memory wiping and proofs of secure erasure

  Amodo's inventory of a GB200 system lists many memory stores beyond GPU HBM and host DRAM. It notes that SSD controller DRAM sits on a private bus that host commands cannot read or write, and that its optimized algorithm leaves 25 GiB of HBM unattested. It also asks how switch memory could be wiped. ^{5 6}

  Scope limitation · Open question. On the record

• Gap between erased and total memory in Memory wiping and proofs of secure erasure

  Bursuc et al. note that memory left between the erased region and the device's full memory could hold data, and that their bounds are tighter only against a restricted adversary. ³

  Scope limitation · Theoretical argument. 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. ^{14 16}

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

• 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. ¹⁸

  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. ¹⁸

  Scope limitation · Open question. On the record

Open questions

• 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. ^{18 21 22}

  Open question · Theoretical argument. On the record

Not yet demonstrated

Proposed Memory wiping and proofs of secure erasure

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

• Research demo Timed challenge-response and memory-occupation challenges Add
  • Bears on the open significant failure “Outside help during challenges” in Memory wiping and proofs of secure erasure. Timed challenges bound how far away a helper can be by how quickly it must answer.
  • Memory wiping and proofs of secure erasure waits on it. Timed challenges must exclude remote memory and other helpers.
• Proposed Network taps and certifiers Add
  • Bandwidth limits and compartmentalization waits on it. The verifier must know that all traffic leaving a pod crosses the capped, monitored links.

Missing prerequisites

• Research demo Timed challenge-response and memory-occupation challenges needed by Memory wiping and proofs of secure erasure Add

Blockers

11 blockers recorded

• Memory wiping and proofs of secure erasure
  • Wipes take time: tens of minutes for a pod's volatile memory and hours for SSDs, displacing work. Performance & compatibility ^{4 5 6}
  • Timed challenges must exclude remote memory and other helpers. Coverage & hidden compute. Waits on Timed challenge-response and memory-occupation challenges ^{3 4}
  • All memory stores in a system must be inventoried and wiped at the same time. Coverage & hidden compute ⁵
• Tamper evidence for verifier devices
  • No tamper-evident enclosure has been designed for AI verifier hardware at retrofit scale. Hardware trust ⁴
  • Battery-backed designs add bulk, limit operating temperature (+10 °C to +35 °C for the IBM 4765) and complicate transport. Performance & compatibility ¹⁰
  • Active monitoring needs power, and visual inspection of large enclosures faces access limits. Access & governance ¹⁴
  • No evaluation has been published in the AI verification setting. Adversarial validation ⁴
• Bandwidth limits and compartmentalization
  • No cap that a verifier can check has been implemented or red-teamed. Adversarial validation ¹⁸
  • The verifier must know that all traffic leaving a pod crosses the capped, monitored links. Coverage & hidden compute. Waits on Network taps and certifiers ⁴
  • 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 ^{18 20}
  • Advances in low-communication training could shrink the margin that the cap enforces. Capacity bounds ^{18 21 22}

From the family or selected implementation's record. ?

Model weights

Not involved: Memory wiping and proofs of secure erasure, Tamper evidence for verifier devices and Bandwidth limits and compartmentalization.

Inputs and outputs

Not involved: Memory wiping and proofs of secure erasure, Tamper evidence for verifier devices and Bandwidth limits and compartmentalization.

Training data

Not involved: Memory wiping and proofs of secure erasure, Tamper evidence for verifier devices and Bandwidth limits and compartmentalization.

Memory wiping and proofs of secure erasure

• Proposed AI 2040 inference-only verification stack Proposed architecture, AI Futures Project
• Proposed Low-trust AI compute verification system overview Proposed architecture, Machine Intelligence Research Institute

Tamper evidence for verifier devices

• 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

1. Verification Plan, R. Dean (2026). Original
2. Secure Code Update for Embedded Devices via Proofs of Secure Erasure, D. Perito & G. Tsudik (2010). Original
3. Software-Based Memory Erasure with Relaxed Isolation Requirements, S. Bursuc et al. (2024). Original
4. A System Overview for Near-Term, Low-Trust AI Compute Verification, N. Cankaya (2026). Original
5. Memory Wipes - Performance Analysis, Amodo Design (2026). Original
6. Improving Disk Wiping Speed for Memory Wipes, Amodo Design (2026). Original
7. Amodo-Design/PoSE-Memory-Wiping (GitHub repository), Amodo Design (2026). Original
8. Empirical Evaluation of Memory-Erasure Protocols, R. Gil-Pons et al. (2025). Original
9. Anti-Tamper Radio: System-Level Tamper Detection for Computing Systems, P. Staat et al. (2022). Original
10. Secure Physical Enclosures from Covers with Tamper-Resistance, V. Immler et al. (2019). Original
11. ImpedanceVerif: On-Chip Impedance Sensing for System-Level Tampering Detection, T. Mosavirik et al. (2023). Original
12. IBM 4765 Cryptographic Coprocessor Security Module: Security Policy, IBM Corporation (2012). Original
13. PHYSEC SEAL: Change detection for maximum safety, PHYSEC GmbH (2026). Original
14. Tamper-Indicating Enclosures, A Current Survey, H. A. Smartt & Z. N. Gastelum (2015). 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. Traffic Shaping for Workload Classification, Lucid Computing (2026). Original
19. De-risking Interconnect Limits for AI Verification, A. Scher et al. (2026). Original
20. The Tray as a Bandwidth Boundary, Amodo Design (2026). Original
21. DiLoCo: Distributed Low-Communication Training of Language Models, A. Douillard et al. (2024). Original
22. Does Distributed Training Undermine Compute Governance?, R. Rahman (2026). Original