Model Transparency: Supply Chain Security for ML

Summary

Model Transparency is Sigstore's open source solution to the growing problem of ML supply chain attacks and model provenance tracking. Just as software packages need cryptographic signing to verify their integrity, machine learning models require similar security measures—but with unique challenges around model artifacts, training data lineage, and deployment pipelines. This tool extends Sigstore's proven cryptographic infrastructure to create tamper-evident records for ML models, enabling teams to verify model authenticity, track provenance, and detect unauthorized modifications throughout the model lifecycle.

The ML Supply Chain Security Problem

Traditional software security focuses on code repositories and package managers, but ML introduces entirely new attack vectors. Models can be poisoned during training, backdoors can be embedded in model weights, and malicious actors can substitute legitimate models with compromised versions. Unlike traditional software, ML models are often distributed as binary artifacts with opaque internals, making tampering difficult to detect.

Model Transparency addresses these challenges by creating cryptographic signatures for model artifacts at key points in the ML pipeline—from training completion to deployment. The tool integrates with popular ML frameworks and model registries, automatically generating verifiable attestations that include model metadata, training provenance, and dependency information.

Core Features and Capabilities

• Cryptographic Model Signing: Leverages Sigstore's certificate-based signing to create tamper-evident seals for model files, ensuring any unauthorized modifications are detectable.
• Provenance Tracking: Automatically captures and cryptographically binds metadata about training data sources, framework versions, hardware configurations, and training procedures to model artifacts.
• Integration-First Design: Works with existing ML toolchains including MLflow, Weights & Biases, Hugging Face Hub, and major cloud ML platforms without requiring workflow overhauls.
• Transparency Log: All model signatures are recorded in a public, immutable transparency log (similar to Certificate Transparency for web PKI), enabling ecosystem-wide visibility into model provenance.
• Verification APIs: Provides simple APIs and CLI tools for downstream consumers to verify model authenticity before loading or deployment, with clear pass/fail results.

Who This Resource Is For

• ML Platform Engineers building internal model registries and deployment pipelines who need to implement security controls around model distribution and prevent unauthorized model substitution.
• Security Teams at organizations using third-party models or operating in regulated industries where model provenance and integrity verification are compliance requirements.
• Open Source ML Projects that distribute pre-trained models and want to provide users with cryptographic guarantees about model authenticity and build provenance.
• MLOps Teams implementing CI/CD for machine learning who need to integrate security checkpoints into automated training and deployment workflows.
• AI Red Teams and Researchers studying ML supply chain attacks who need tools to demonstrate vulnerabilities and validate security controls.

Getting Started with Implementation

Begin by installing the Model Transparency CLI and integrating it into your model training pipeline at the point where final model artifacts are saved. The tool can sign models automatically as part of your MLOps workflow or be invoked manually for ad-hoc signing.

For verification, implement checks at model loading time in your inference services or deployment scripts. The verification process is designed to be fast and lightweight, suitable for runtime checks without significant performance impact.

The project provides examples for common scenarios including containerized model deployment, serverless inference, and edge deployment where connectivity to the transparency log may be intermittent.

Technical Considerations and Limitations

Model Transparency requires network connectivity to Sigstore's public infrastructure for signing and verification, which may not be suitable for air-gapped environments. However, the project roadmap includes support for private Sigstore deployments.

Large model files (multi-GB transformer models) require careful handling of the signing process, as the tool needs to compute cryptographic hashes over the entire model artifact. The project provides guidance on optimizing this for different storage backends.

The tool currently focuses on model artifacts themselves rather than training data provenance—while it can record metadata about data sources, it doesn't provide cryptographic guarantees about training data integrity or licensing compliance.

FAQ

Q: Does this work with models trained on proprietary datasets?

• Q: What happens if Sigstore's infrastructure is unavailable?
• Q: Can this detect model backdoors or poisoning?