Proposal Explorer

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Timed challenge-response and memory-occupation challenges and Bandwidth limits and compartmentalization

No new hardware needed

Safeguard attestation, Timed challenge-response and memory-occupation challenges and On-chip telemetry from timing, memory and performance counters

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.

• Safeguard attestation: Analysis
• Timed challenge-response and memory-occupation challenges: Analysis
• On-chip telemetry from timing, memory and performance counters: Red-teamed
• Bandwidth limits and compartmentalization: Analysis

Open significant failures

7 failures in 4 mechanisms

• Components outside the attested boundary in Safeguard attestation

  In the proof-of-guardrail experiments, the guardrail model and the agent's backend model were both reached through external APIs, and the authors leave the decision to trust those APIs to the verifier. The measured wrapper must also have no vulnerability that lets the unmeasured agent bypass the guardrail, for example by executing arbitrary commands inside the enclave. The code's README states that the enclave does not currently restrict the agent's arbitrary command execution, which could be used to bypass guardrails. ^{1 2}

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

• Memory-bus interposition extracts attestation keys and forges attestations in Safeguard attestation

  Applies to variants using the affected Intel or AMD trust roots. PAL*M excludes physical attacks. A TDX-backed safeguard claim against a physical host attacker would be defeated, but these studies do not demonstrate a break of the AWS Nitro proof-of-guardrail prototype or of verifier-side recomputation.

  The TEE findings cover DDR5 attacks on Intel TDX, the H100 relay demonstration, DDR4 attacks on AMD SEV-SNP, and software-only SEV-SNP forgery before AMD's fixes. These are inherited hardware limits; a governance analysis explains why physical access matters in a treaty setting. ^{1 4 7 8 9 10 11 12 13}

  Response: Intel and AMD place the physical attack class outside their threat models, according to the researchers. AMD reports firmware fixes for RMPocalypse.

  Known failure · Demonstrated attack · Significant · Open · Inherited finding. On the record · Related finding in TEE remote attestation for AI workloads

  Related mechanism Proposed Hardware-enabled guarantees (flexHEG) and guarantee processors: A tamper-protected enclosure around the chip is the proposed answer when the party that holds the hardware may attack it physically. A pointer, not evidence that this failure is mitigated. Add

• Remote memory narrows the timing margin in Timed challenge-response and memory-occupation challenges

  Data-centre remote memory access returns in about 1–2 µs, against about 70–200 ns for local DRAM. The MIRI overview says verification of memory saturation depends on ruling out remote access by latency or physical disconnection. It names pre-staging data into local memory as the remaining evasion and proposes an unpredictable, capacity-filling challenge to close it. ¹³

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

  Related mechanism Research demo Bandwidth limits and compartmentalization: Physical disconnection is proposed to exclude remote memory between the separated groups during a challenge. It depends on the isolation boundary being enforced. A pointer, not evidence that this failure is mitigated. In the proposal.

• 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. ²⁴

  Known failure · Demonstrated attack · 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. ^{9 25}

  Known failure · Theoretical argument · Significant · Open. 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. ²⁶

  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. ²⁸

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

Scope limitations

• Attestation shows a safeguard ran, not that it is effective in Safeguard attestation

  Proof of guardrail ensures that the guardrail executed, but the guardrail can still err or be jailbroken. Because the guardrail must be open source, a malicious developer can attack it with jailbreaks while still presenting a valid proof. In the authors' evaluation, Llama Guard 3 reached an F1 score of 0.56 on the unsafe class of the ToxicChat dataset. The authors state that proof of guardrail should not be interpreted or advertised as proof of safety. ¹

  Scope limitation · Theoretical argument. On the record

• Selective attestation leaves traffic uncovered in Safeguard attestation

  Attestations are issued per response. In the prototype, the agent offers them when it receives high-stakes questions, so nothing shows that unattested traffic went through the same path. PAL*M's authors note that a prover could cherry-pick favourable executions, and suggest verifier-published nonces or requesting only session-level proofs. A governance analysis notes that auditors also need assurance that all activity is accounted for, since a host could start a second confidential virtual machine that bypasses monitoring. ^{1 4 9}

  Scope limitation · Theoretical argument. On the record

  Related mechanism Research demo On-chip telemetry from timing, memory and performance counters: On-chip counters are a proposed route to evidence about everything a chip runs, including a second virtual machine that skips the safeguard. A pointer, not evidence that this failure is mitigated. In the proposal.

• Measurements may omit behaviour-relevant configuration or runtime changes in Safeguard attestation

  Every component that influences inference behaviour must be covered by the launch measurement, including feature flags, environment variables and invocation arguments. A launch measurement also does not show that a program keeps running as measured if the kernel is later compromised. ⁹

  Scope limitation · Theoretical argument. On the record

• 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. ^{15 24}

  Scope limitation · Theoretical argument. On the record

  Related mechanism Proposed 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 failure is mitigated. Add

• 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. ¹⁵

  Scope limitation · Theoretical argument. 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. ²⁶

  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. ²⁶

  Scope limitation · Open question. On the record

Open questions

• Error rates not quantified in Timed challenge-response and memory-occupation challenges

  Monfared et al. show separable timing distributions. Their acceptance rule passes a GPU when its mean time per round stays at or below a chosen maximum, and an appendix outlines statistical tests for the proof-of-work puzzle. They leave hardware-specific threshold values to future work and report no false-positive or false-negative rates. ¹⁵

  Open question · Open question. On the record

• 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. ¹⁵

  Open question · Open question. 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. ^{26 29 30}

  Open question · Theoretical argument. On the record

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

• Proposed Hardware-enabled guarantees (flexHEG) and guarantee processors Add
  • Bears on the open significant failure “Memory-bus interposition extracts attestation keys and forges attestations” in Safeguard attestation. A tamper-protected enclosure around the chip is the proposed answer when the party that holds the hardware may attack it physically.
  • On-chip telemetry from timing, memory and performance counters waits on it. Shipping accelerators need a tamper-resistant, authenticated telemetry path.
• Operational use Hardware-attested weight binding Add
  • Safeguard attestation waits on it. Safeguard evidence must be bound to the model actually served, which depends on model-identity attestation.
• Operational use TEE remote attestation for AI workloads Add
  • Safeguard attestation waits on it. Frontier model inference typically needs several GPUs, GPU confidential computing is less mature than CPU support, and CPU inference, which an enclave prototype had to use, ran about 100 times slower than GPU inference.
  • Safeguard attestation waits on it. Trust rests on a small number of hardware vendors, and a per-CPU Intel attestation key has been extracted by physical attack.
  • On-chip telemetry from timing, memory and performance counters depends on it.
• Research demo Tamper evidence for verifier devices Add
  • Bandwidth limits and compartmentalization waits on it. Shaping devices and routing assignments must be trusted by both parties; Amodo has not yet fully analysed resilience to a compromised DPU.
• 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

• Operational use TEE remote attestation for AI workloads needed by Safeguard attestation and On-chip telemetry from timing, memory and performance counters Add
• Operational use Hardware-attested weight binding needed by Safeguard attestation Add
• Research demo Tamper evidence for verifier devices needed by Bandwidth limits and compartmentalization Add

Shared foundations

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

Blockers

16 blockers recorded

• Safeguard attestation
  • No published design shows that all of a provider's traffic passes through the attested safeguard path; current evidence covers individual attested responses. Coverage & hidden compute ^{1 9}
  • Frontier model inference typically needs several GPUs, GPU confidential computing is less mature than CPU support, and CPU inference, which an enclave prototype had to use, ran about 100 times slower than GPU inference. Performance & compatibility. Waits on TEE remote attestation for AI workloads ^{9 31}
  • Trust rests on a small number of hardware vendors, and a per-CPU Intel attestation key has been extracted by physical attack. Hardware trust. Waits on TEE remote attestation for AI workloads ^{7 9}
  • Safeguard evidence must be bound to the model actually served, which depends on model-identity attestation. Evidence binding. Waits on Hardware-attested weight binding ^{31 32}
  • No independent red-team or audit of a safeguard-attestation system has been published, and the available prototypes are described by their authors as proofs of concept that have not been stress-tested by a counterparty. Adversarial validation ^{2 6}
• Timed challenge-response and memory-occupation challenges
  • No network-level memory challenge across data-centre servers has been demonstrated. Adversarial validation ¹³
  • Challenges that fill memory displace workloads; filling a pod's volatile memory takes tens of minutes and SSDs take hours. Performance & compatibility ^{13 15}
  • Outside help, such as remote memory, must be excluded during challenges. Coverage & hidden compute. Waits on Bandwidth limits and compartmentalization ¹³
• 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 ^{23 24}
  • 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 ^{9 25}
  • Continuous challenge puzzles cost power and throughput on production workloads. Performance & compatibility ¹⁵
  • Evaluation has not gone beyond single nodes, framework-level evasion and one vendor's hardware. Adversarial validation ²⁴
• Bandwidth limits and compartmentalization
  • No cap that a verifier can check has been implemented or red-teamed. Adversarial validation ²⁶
  • The verifier must know that all traffic leaving a pod crosses the capped, monitored links. Coverage & hidden compute. Waits on Network taps and certifiers ¹³
  • 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 ^{26 28}
  • Advances in low-communication training could shrink the margin that the cap enforces. Capacity bounds ^{26 29 30}

From the family or selected implementation's record. ?

Model weights

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

Not involved: Timed challenge-response and memory-occupation challenges and Bandwidth limits and compartmentalization.

Inputs and outputs

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

Not involved: Timed challenge-response and memory-occupation challenges and Bandwidth limits and compartmentalization.

Training data

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

Not involved: Safeguard attestation, Timed challenge-response and memory-occupation challenges and Bandwidth limits and compartmentalization.

Safeguard attestation

None on the map

Timed challenge-response and memory-occupation challenges

• Proposed Data-centre memory challenging Proposed architecture, Machine Intelligence Research Institute
• Research demo GPU contention probes Research prototype
• Proposed Low-trust AI compute verification system overview Proposed architecture, Machine Intelligence Research Institute
• Research demo SAGE Research prototype
• Research demo VRAM-residency challenge Research prototype

On-chip telemetry from timing, memory and performance counters

None on the map

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. Proof-of-Guardrail in AI Agents and What (Not) to Trust from It, X. Jin et al. (2026). Original
2. Verifiable-ClawGuard: proof-of-guardrail reference code, SaharaLabsAI (2026). Original
3. Safety Without Compromising on Privacy, D. McCann-Sayles et al. (2026). Original
4. PAL*M: Property Attestation for Large Generative Models, P. Chantasantitam et al. (2026). Original
5. Enabling Verifiably-Scoped Monitoring through Large Language Models and Trusted Compute, B. Penchas et al. (2026). Original
6. Auditor-in-a-Box: Tools for Third-Party Auditing, R. Rinberg & B. Penchas (2026). Original
7. TEE.fail: Breaking Trusted Execution Environments via DDR5 Memory Bus Interposition, J. Chuang et al. (2026). Original
8. DDRop: Active Memory Interposer Attacks on Confidential VMs by Dropping DDR5 Writes, J. De Meulemeester et al. (2026). Original
9. On TEEs for Privacy-Preserving Monitoring in AI Governance, Gloria Z (2026). Original
10. Battering RAM: Low-Cost Interposer Attacks on Confidential Computing via Dynamic Memory Aliasing, J. De Meulemeester et al. (2026). Original
11. RMPocalypse: How a Catch-22 Breaks AMD SEV-SNP, B. Schlüter & S. Shinde (2025). Original
12. SEV-SNP RMP Initialization Vulnerability (AMD-SB-3020), AMD (2025). Original
13. A System Overview for Near-Term, Low-Trust AI Compute Verification, N. Cankaya (2026). Original
14. Verification Plan, R. Dean (2026). Original
15. Timing and Memory Telemetry on GPUs for AI Governance, S. K. Monfared et al. (2026). Original
16. SAGE: Software-based Attestation for GPU Execution, A. Ivanov et al. (2023). Original
17. SWATT: SoftWare-based ATTestation for Embedded Devices, A. Seshadri et al. (2004). Original
18. Proofs of Space, S. Dziembowski et al. (2015). Original
19. Secure Code Update for Embedded Devices via Proofs of Secure Erasure, D. Perito & G. Tsudik (2010). Original
20. Software-Based Memory Erasure with Relaxed Isolation Requirements, S. Bursuc et al. (2024). Original
21. On the Difficulty of Software-Based Attestation of Embedded Devices, C. Castelluccia et al. (2009). Original
22. Refutation of "On the Difficulty of Software-Based Attestation of Embedded Devices", A. Perrig & L. van Doorn (2010). Original
23. Guaranteeable Memory: An HBM-Based Chiplet for Verifiable AI Workloads, J. Petrie (2025). Original
24. Detecting Hidden ML Training With Zero-Overhead Telemetry, R. Rahman & S. Tajdari (2026). Original
25. NVIDIA Secure AI with Blackwell and Hopper GPUs (White Paper), NVIDIA (2025). Original
26. Traffic Shaping for Workload Classification, Lucid Computing (2026). Original
27. De-risking Interconnect Limits for AI Verification, A. Scher et al. (2026). Original
28. The Tray as a Bandwidth Boundary, Amodo Design (2026). Original
29. DiLoCo: Distributed Low-Communication Training of Language Models, A. Douillard et al. (2024). Original
30. Does Distributed Training Undermine Compute Governance?, R. Rahman (2026). Original
31. Attestable Audits: Verifiable AI Safety Benchmarks Using Trusted Execution Environments, C. Schnabl et al. (2025). Original
32. How Tinfoil Proves Exactly What Model Is Running, Tinfoil Team (2026). Original