EnclaveX: End-to-End Confidential AI with CPU/GPU TEEs
For cloud AI users, this work provides a practical evaluation of integrated CPU/GPU TEEs, highlighting security gaps and performance trade-offs.
This paper presents an end-to-end workflow combining CPU and GPU TEEs for confidential AI, evaluating performance overhead with Intel TDX and NVIDIA H200 GPUs. It identifies vulnerabilities like Kubernetes admin access to confidential VM contents.
Large Language Models (LLMs) have rapidly proliferated, driving widespread adoption of AI applications. Most deployments rely on centralized infrastructures such as Microsoft Azure, Google Cloud, or AWS, requiring users to share sensitive data and training or fine-tuning code. This dependence raises significant security and privacy concerns, as cloud providers must be trusted to ensure confidentiality and integrity. Trusted Execution Environments (TEEs) e.g., Intel SGX/TDX, AMD SEV-SNP, and ARM CCA have been introduced to mitigate these risks. More recently, NVIDIA has developed GPU TEEs (e.g., H100/H200), yet comprehensive evaluations of end-to-end workflows that integrate CPU and GPU TEEs remain limited. Critical aspects, including performance overhead, remote attestation, and security guarantees for AI/LLM applications, have not been sufficiently studied. This paper addresses this gap by presenting an end-to-end workflow that combines CPU and GPU TEEs. We propose mechanisms to ensure confidentiality and integrity at both the VM level (via Intel TDX and AMD SEV-SNP) and the application level, highlighting vulnerabilities such as Kubernetes administrators' ability to access confidential VM contents. Finally, we evaluate the performance overhead of our system using industry benchmarks, focusing on configurations that integrate Intel TDX with NVIDIA H200 GPUs.