Intel TDX Demystified: A Top-Down Approach
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Abstract (arXiv)
Intel Trust Domain Extensions (TDX) is a new architectural extension in the 4th Generation Intel Xeon Scalable Processor that supports confidential computing. TDX allows the deployment of virtual machines in the Secure-Arbitration Mode (SEAM) with encrypted CPU state and memory, integrity protection, and remote attestation. TDX aims to enforce hardware-assisted isolation for virtual machines and minimize the attack surface exposed to host platforms, which are considered to be untrustworthy or adversarial in the confidential computing's new threat model. TDX can be leveraged by regulated industries or sensitive data holders to outsource their computations and data with end-to-end protection in public cloud infrastructure. This paper aims to provide a comprehensive understanding of TDX to potential adopters, domain experts, and security researchers looking to leverage the technology for their own purposes. We adopt a top-down approach, starting with high-level security principles and moving to low-level technical details of TDX. Our analysis is based on publicly available documentation and source code, offering insights from security researchers outside of Intel.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Mar 2023.
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Citing papers per year: 2024: 2; 2025: 3; 2026: 2.
7 papers.
- 2025Empowering Edge Intelligence: A Comprehensive Survey on On-Device AI Modelscited by 44
- 2025Confidential LLM Inference: Performance and Cost Across CPU and GPU TEEscited by 5
- 2024Fortify Your Foundations: Practical Privacy and Security for Foundation Model Deployments In The Cloudcited by 4
- 2025Trusted AI Agents in the Cloudcited by 4
- 2024Props for Machine-Learning Securitycited by 0
- 2026Committed SAE-Feature Traces for Audited-Session Substitution Detection in Hosted LLMscited by 0
- 2026SpliTEE: Fast and Private LLM Inference by Coupling GPU-Assisted Trusted Execution Environments with Differential Privacycited by 0
- 2026SpliTEE: Fast and Private LLM Inference by Coupling GPU-Assisted Trusted Execution Environments with Differential Privacy14 Sep 2026
- 2026Committed SAE-Feature Traces for Audited-Session Substitution Detection in Hosted LLMs20 Apr 2026
- 2025Trusted AI Agents in the Cloud5 Dec 2025
- 2025Confidential LLM Inference: Performance and Cost Across CPU and GPU TEEs23 Sep 2025
- 2025Empowering Edge Intelligence: A Comprehensive Survey on On-Device AI Models8 Mar 2025
- 2024Props for Machine-Learning Security27 Oct 2024
- 2024Fortify Your Foundations: Practical Privacy and Security for Foundation Model Deployments In The Cloud8 Oct 2024