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Near-Term Enforcement of AI Chip Export Controls Using A Firmware-Based Design for Offline Licensing

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arxiv 2404.18308 v2 pith:3UQDF7Z7 submitted 2024-04-28 cs.CR cs.CY

classification cs.CRcs.CY
keywords licensingofflinechipssecurityfirmware-basedhardwaremechanismchip
verification ladder T0 review T1 audit T2 compute T3 formal
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Offline Licensing is a mechanism for compute governance that could be used to prevent unregulated training of potentially dangerous frontier AI models. The mechanism works by disabling AI chips unless they have an unused license from a regulator. In this report, we present a design for a minimal version of Offline Licensing that could be delivered via a firmware update. Existing AI chips could potentially support Offline Licensing within a year if they have the following (relatively common) hardware security features: firmware verification, firmware rollback protection, and secure non-volatile memory. Public documentation suggests that NVIDIA's H100 AI chip already has these security features. Without additional hardware modifications, the system is susceptible to physical hardware attacks. However, these attacks might require expensive equipment and could be difficult to reliably apply to thousands of AI chips. A firmware-based Offline Licensing design shares the same legal requirements and license approval mechanism as a hardware-based solution. Implementing a firmware-based solution now could accelerate the eventual deployment of a more secure hardware-based solution in the future. For AI chip manufacturers, implementing this security mechanism might allow chips to be sold to customers that would otherwise be prohibited by export restrictions. For governments, it may be important to be able to prevent unsafe or malicious actors from training frontier AI models in the next few years. Based on this initial analysis, firmware-based Offline Licensing could partially solve urgent security and trade problems and is technically feasible for AI chips that have common hardware security features.

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Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Hardware Mechanisms to Dynamically Throttle AI Performance

    cs.AR 2026-07 conditional novelty 6.0 of 10

    Dynamic microarchitecture throttling of GPU memory resources can cut LLM inference performance by up to 80% with low hardware overhead, giving architects a continuous, hardware-enforced AI capability control.

  2. How to Catch a GPU: A Taxonomy of Verification and Enforcement Mechanisms for International AI Agreements

    cs.CY 2026-06 conditional novelty 6.0 of 10

    Verification of international AI agreements will fail first at detecting hidden compute facilities, around the 10,000-H100-equivalent scale, before other enforcement mechanisms break.

  3. Technical Options for Flexible Hardware-Enabled Guarantees

    cs.CR 2025-06 conditional novelty 6.0 of 10

    A hardware 'interlock' placed on AI accelerator network paths could provide privacy-preserving, verifiable guarantees about AI compute usage, according to a design analysis that sketches FLOP-counting and update protocols.

  4. Toward a Global Regime for Compute Governance: Building the Pause Button

    cs.CY 2025-06 conditional novelty 4.0 of 10

    The paper argues that a global, enforceable compute pause is achievable through a layered framework of hardware controls, supply chain tracking, and regulation.

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