ACAI: Protecting Accelerator Execution with Arm Confidential Computing Architecture
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Abstract (arXiv)
Trusted execution environments in several existing and upcoming CPUs demonstrate the success of confidential computing, with the caveat that tenants cannot securely use accelerators such as GPUs and FPGAs. In this paper, we reconsider the Arm Confidential Computing Architecture (CCA) design, an upcoming TEE feature in Armv9-A, to address this gap. We observe that CCA offers the right abstraction and mechanisms to allow confidential VMs to use accelerators as a first-class abstraction. We build ACAI, a CCA-based solution, with a principled approach of extending CCA security invariants to device-side access to address several critical security gaps. Our experimental results on GPU and FPGA demonstrate the feasibility of ACAI while maintaining security guarantees.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 25 May 2023.
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Citing papers per year: 2024: 2; 2025: 0; 2026: 2.
4 papers.
- 2024TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Modelscited by 3
- 2024Confidential Prompting: Privacy-preserving LLM Inference on Cloudcited by 1
- 2026When Agents Handle Secrets: A Survey of Confidential Computing for Agentic AIcited by 0
- 2026ACLE-MCP: Attested Capability Leases for Execution-Time Trust in Remote LLM Tool Usecited by 0
- 2026ACLE-MCP: Attested Capability Leases for Execution-Time Trust in Remote LLM Tool Use2 Sep 2026
- 2026When Agents Handle Secrets: A Survey of Confidential Computing for Agentic AI4 May 2026
- 2024TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models15 Nov 2024
- 2024Confidential Prompting: Privacy-preserving LLM Inference on Cloud27 Sep 2024