Amean Asad

Amean Asad, André Arko

Kettle is an attested build system that produces cryptographically verifiable provenance for software built inside Trusted Execution Environments (TEEs). A Kettle build records the source commit, dependency set, toolchain, build environment, and output artifact digests in a provenance document produced inside a measured confidential VM. The SHA-256 digest of that document is committed to the TEE platform's attestation report-data field, so the hardware-signed attestation report is itself the signature on the provenance, with the signing identity chaining to the TEE manufacturer's root of trust rather than to the build infrastructure operator. Because the CVM image is itself reproducible, its launch measurement is public and stable, which lets a build requester pre-attest the CVM before submitting any input and optionally deliver source over a TLS channel terminated inside it, so the build runs end-to-end confidentially without the host ever seeing source code in plaintext. Verification reduces to one signature check against the vendor root and a small set of digest comparisons, with no need to re-execute the build. The result removes the build infrastructure, its operators, and the artifact distribution channel from the trust surface a verifier must accept when deciding whether a binary corresponds to its claimed inputs.

Amean Asad, Patrick McClurg, João Andrade

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.