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Patrick McClurg

Publications and source records attributed to Patrick McClurg.

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AMD SEV-SNP: A Confidential Computing Primer

This paper is a technical primer on AMD Secure Encrypted Virtualization with Secure Nested Paging (SEV-SNP), a hardware confidential computing implementation that provides Trusted Execution Environments (TEEs) for virtual machines. SEV-SNP treats the hypervisor as adversarial. It encrypts guest memory and register state with keys the hypervisor never possesses, detects any tampering with guest memory at the point of access, and lets a guest prove to a remote verifier exactly what code it is running. The paper constructs each of these guarantees from the hardware up. It opens with the threat model that drives the design and the hardware that enforces it, the AMD Secure Processor and the encryption engine in the memory controller. It then develops the mechanisms that make a confidential guest practical. The Reverse Map Table provides memory integrity against an adversary who controls the page tables. The privilege and communication machinery (VM Privilege Levels, the encrypted VM Save Area, and the GHCB protocol) lets the guest cooperate with a hypervisor it does not trust. The attestation pipeline binds a hardware-signed measurement of the guest's initial state to AMD's certificate chain, so a remote verifier can confirm independently what is running.

cs.CR

C8s: A Confidential Kubernetes Architecture

This paper presents C8s, a confidential computing architecture for Kubernetes that provides cryptographically rooted confidentiality, integrity, and verifiability guarantees for Kubernetes clusters from infrastructure operators. These guarantees are cryptographically provable to any independent third party verifier. The architecture is built on hardware Trusted Execution Environments (TEEs), specifically AMD SEV-SNP, Intel TDX, and NVIDIA Confidential Computing support, to establish an attestation-rooted trust boundary around confidential VMs. This design is compatible with managed Kubernetes services such as Amazon EKS, Google GKE, and Microsoft AKS, where the control plane cannot be attested. Under this boundary, three groups gain guarantees that are absent from conventional deployments. Data and artifact owners can deploy sensitive workloads and proprietary artifacts on third-party infrastructure without risking exfiltration. Compute providers can offer execution services without revealing workloads to cloud operators. End users can submit requests that remain opaque to all parties except the attested TEE processing them. Representative workloads include AI inference, securing AI model weights, and training or fine-tuning on sensitive data.

cs.CR