Artifact Signing

Artifact signing uses asymmetric cryptography to bind a digital artifact to a specific publisher identity. The signer generates a private key to create the signature and publishes a public key for verification. This ensures that any consumer can cryptographically prove the artifact originated from the trusted source and has not been modified in transit or at rest. Common standards include Sigstore for OCI containers and in-toto for supply chain attestations.

Once signed, any modification to the artifact—whether a single weight in a model checkpoint or a line of training code—invalidates the cryptographic signature. Verification tools compute a cryptographic hash of the artifact and compare it against the signed digest. A mismatch immediately signals tampering. This property is critical for defending against data poisoning and model supply chain attacks where adversaries attempt to inject backdoors by replacing legitimate artifacts with compromised versions.

Modern signing frameworks extend beyond simple artifact signatures to include in-toto attestations—signed metadata documents that capture the full provenance of an artifact. These attestations record:

• Materials: The inputs used (datasets, base models, hyperparameters)
• Steps: The processes executed (training scripts, evaluation harnesses)
• Products: The outputs generated (model weights, evaluation reports) This creates a verifiable, end-to-end record of how an artifact was produced, enabling auditors to trace any model back to its exact training pipeline.

The security of artifact signing depends entirely on key management. Best practices include:

• Hardware security modules (HSMs) for private key storage
• Short-lived ephemeral keys generated per signing event
• Transparency logs (e.g., Rekor) that publish signatures to an immutable, append-only ledger
• Certificate authorities or OIDC-based identity for binding keys to verified identities Compromise of a signing key allows an attacker to sign malicious artifacts that appear legitimate, making key hygiene the foundation of the entire trust model.

Artifact signing integrates directly into CI/CD and MLOps pipelines to automate integrity verification at every stage. Common integration points include:

• Model registries: Signing model weights upon registration and verifying signatures before deployment
• Container registries: Signing Docker images that package inference servers
• Dataset versioning: Signing dataset snapshots to prevent undetected tampering Tools like Cosign and Notation provide CLI and library interfaces for seamless integration with existing workflows, making signing a transparent step in the release process.

Signing enables automated policy enforcement at deployment time. Organizations define policies such as:

• Only deploy artifacts signed by the ML engineering team
• Require attestations proving the artifact passed adversarial robustness testing
• Block any artifact whose signature is not present in the transparency log Admission controllers in Kubernetes (e.g., Kyverno, OPA/Gatekeeper) can enforce these policies, ensuring that only verified, attested artifacts reach production environments.

The holistic practice of ensuring the integrity, authenticity, and provenance of every component in the ML lifecycle. This spans pre-trained weights, datasets, software dependencies, and deployment configurations. A breach at any link—such as a poisoned base model from an unverified registry—can compromise downstream fine-tuning. Artifact signing is the foundational cryptographic primitive that enables automated policy enforcement across this chain.

The documented, cryptographically verifiable lineage and chain of custody of a dataset. Provenance records capture the origin, transformation steps, and responsible entities for each data version. When combined with artifact signing, provenance metadata is included in the signed payload, creating a tamper-evident audit trail. This allows downstream consumers to verify that a dataset was sourced from an authorized, trusted origin and has not been silently modified.

A formal, machine-readable inventory of all components, libraries, and dependencies that constitute a software artifact. For ML systems, an AI BOM extends this to include datasets, pre-trained models, and training pipelines. When an SBOM is included in a signed artifact, verifiers can cryptographically attest to the exact composition of a model at release time, enabling rapid vulnerability assessment when a dependency is later found to be compromised.

Hardware-enforced isolated compute enclaves that protect data and code during active processing. TEEs provide attestation—a signed proof of the enclave's identity and the code it is running. When combined with artifact signing, a remote party can verify that a model was loaded into a genuine TEE and that inference results were produced by the exact signed model binary, not a substitute, ensuring end-to-end confidentiality and integrity.