A command-line interface tool for creating, managing, and verifying Content Provenance and Authenticity (C2PA) manifests for machine learning models, datasets, and related artifacts.
- Model & Dataset Manifests: Create C2PA-compliant manifests for ML models and datasets
- Cryptographic Signing: Sign manifests with cryptographic keys for authenticity verification
- Provenance Linking: Create verifiable links between models, datasets, and ML assets
- Multiple Storage Types: Store manifests in MongoDB, Rekor log, or filesystem backends
- Format Support: Work with models in ONNX, TensorFlow, PyTorch, and Keras formats
- TEE Attestation: Optional support for Trusted Execution Environment (TDX) integration
- Rust toolchain (1.70 or later) - Install Rust
- OpenSSL development libraries
- (Optional) Protobuf compiler for TDX support
The simplest way to install Atlas CLI is using cargo:
cargo install atlas-cli
# First install protobuf compiler
# Ubuntu/Debian:
sudo apt install protobuf-compiler
# Then install with TDX feature
cargo install atlas-cli --features with-tdx
# Clone repositories
git clone https://github.com/IntelLabs/atlas-cli
cd atlas-cli
# Build and install
cargo install --path .
# Or build without installing
cargo build --release
# Binary will be at ./target/release/atlas-cli
# To update to the latest version:
cargo install atlas-cli --force
For more detailed information, please refer to:
- User Guide - Installation, configuration, and command reference
- Development Guide - Contributing, building, and architecture
- Examples - Usage examples and workflow patterns
This project is licensed under the Apache 2.0 License - see the LICENSE file for details.
If you use Atlas CLI in your research or work, please cite our paper:
@misc{atlas2025github,
title={Atlas: A Framework for ML Lifecycle Provenance & Transparency},
author={Marcin Spoczynski and Marcela S. Melara and Sebastian Szyller},
year={2025},
eprint={2502.19567},
archivePrefix={arXiv},
primaryClass={cs.CR},
url={https://arxiv.org/abs/2502.19567v1}
}
- Paper: Atlas: A Framework for ML Lifecycle Provenance & Transparency
- Blog Post: Building Trust in AI: An End-to-End Approach for the Machine Learning Lifecycle
- Documentation: [docs.rs/atlas-cli]
- Crate: [crates.io/crates/atlas-cli]