Blueprint, Bootstrap, and Bridge: A Security Look at NVIDIA GPU Confidential Computing
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914483670089728 |
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| author | Gu, Zhongshu Valdez, Enriquillo Ahmed, Salman Stephen, Julian James Le, Michael Jamjoom, Hani Zhao, Shixuan Lin, Zhiqiang |
| author_facet | Gu, Zhongshu Valdez, Enriquillo Ahmed, Salman Stephen, Julian James Le, Michael Jamjoom, Hani Zhao, Shixuan Lin, Zhiqiang |
| contents | NVIDIA GPU Confidential Computing (GPU-CC) aims to provide secure execution for AI workloads. For end users, enabling GPU-CC is seamless and requires no modifications to existing applications. However, this ease of adoption relies on a proprietary and highly complex system that is difficult to inspect, creating challenges for researchers seeking to understand its architecture and security landscape. In this work, we provide a security look at GPU-CC by reconstructing a coherent view of the system. We first examine the system's blueprint, focusing on the specialized architectural engines that support its security mechanisms. We then analyze the bootstrap process, which coordinates hardware and software components to establish these protections. Finally, we conduct targeted experiments to assess whether, under the GPU-CC threat model, data transfers along different paths remain protected across the bridge between trusted CPU and GPU domains. We responsibly disclosed all security findings presented in this paper to the NVIDIA Product Security Incident Response Team (PSIRT). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_02770 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Blueprint, Bootstrap, and Bridge: A Security Look at NVIDIA GPU Confidential Computing Gu, Zhongshu Valdez, Enriquillo Ahmed, Salman Stephen, Julian James Le, Michael Jamjoom, Hani Zhao, Shixuan Lin, Zhiqiang Cryptography and Security NVIDIA GPU Confidential Computing (GPU-CC) aims to provide secure execution for AI workloads. For end users, enabling GPU-CC is seamless and requires no modifications to existing applications. However, this ease of adoption relies on a proprietary and highly complex system that is difficult to inspect, creating challenges for researchers seeking to understand its architecture and security landscape. In this work, we provide a security look at GPU-CC by reconstructing a coherent view of the system. We first examine the system's blueprint, focusing on the specialized architectural engines that support its security mechanisms. We then analyze the bootstrap process, which coordinates hardware and software components to establish these protections. Finally, we conduct targeted experiments to assess whether, under the GPU-CC threat model, data transfers along different paths remain protected across the bridge between trusted CPU and GPU domains. We responsibly disclosed all security findings presented in this paper to the NVIDIA Product Security Incident Response Team (PSIRT). |
| title | Blueprint, Bootstrap, and Bridge: A Security Look at NVIDIA GPU Confidential Computing |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2507.02770 |