cryptotensors 0.2.3

CryptoTensors is an extension of safetensors that adds encryption, signing, and access control (Rego-based policy engine) while maintaining full backward compatibility with the safetensors format

CryptoTensors Python Package

CryptoTensors is a secure tensor file format that extends safetensors with encryption, signing, and access control capabilities while maintaining full backward compatibility.

Requirements

• Python 3.11+ (required for zero-copy buffer protocol support)

Installation

pip install cryptotensors

Transparent Loading of Encrypted Models (Compatible Package)

If you want to load encrypted CryptoTensors models without modifying your code, you can use the compatible package released on GitHub Releases:

# Uninstall the original safetensors package
pip uninstall safetensors

# Install the compatible package directly from GitHub release
# Replace {tag} with the release tag (e.g., v0.1.0)
pip install https://github.com/aiyah-meloken/cryptotensors/releases/download/{tag}/safetensors-0.7.0-py3-none-any.whl

# Example for v0.1.0:
# pip install https://github.com/aiyah-meloken/cryptotensors/releases/download/v0.1.0/safetensors-0.7.0-py3-none-any.whl

After installation, your existing code using from safetensors import ... will transparently support both regular safetensors files and encrypted CryptoTensors files without any code changes. The compatible package uses the safetensors namespace but internally depends on cryptotensors, enabling seamless encryption support.

Usage

Basic Usage (Safetensors Compatible)

CryptoTensors is fully backward compatible with safetensors. You can use it as a drop-in replacement:

Numpy

from cryptotensors.numpy import save_file, load_file
import numpy as np

tensors = {
   "a": np.zeros((2, 2)),
   "b": np.zeros((2, 3), dtype=np.uint8)
}

save_file(tensors, "./model.safetensors")

# Now loading
loaded = load_file("./model.safetensors")

Torch

from cryptotensors.torch import save_file, load_file
import torch

tensors = {
   "a": torch.zeros((2, 2)),
   "b": torch.zeros((2, 3), dtype=torch.uint8)
}

save_file(tensors, "./model.safetensors")

# Now loading
loaded = load_file("./model.safetensors")

Encryption Usage

CryptoTensors adds encryption and signing capabilities:

import torch
from cryptotensors.torch import save_file, load_file

tensors = {
   "weight1": torch.zeros((1024, 1024)),
   "weight2": torch.zeros((1024, 1024))
}

# Encrypt and save
config = {
    "enc_key": enc_key,    # JWK format encryption key
    "sign_key": sign_key,  # JWK format signing key
}
save_file(tensors, "model.cryptotensors", config=config)

# Load encrypted file (keys retrieved from key provider)
tensors = load_file("model.cryptotensors")

See the documentation for detailed guides on encryption, key management, and integration examples.

Features

• 🔐 Encryption: AES-GCM and ChaCha20-Poly1305 encryption for tensor data
• ✍️ Signing: Ed25519 signature verification for file integrity
• 🔑 Key Management: Flexible key provider system (environment variables, files, programmatic)
• 🛡️ Access Policy: Rego-based policy engine for fine-grained access control
• 🔄 Backward Compatible: Works seamlessly with existing safetensors code

Developing

# Install in development mode
pip install -e .[dev]

This should be enough to install this library locally for development.

Testing

# Install with testing dependencies
pip install -e .[dev]

# Run tests
pytest -sv tests/

Citation

This implementation is based on the following research paper:

Zhu, H., Li, S., Li, Q., & Jin, Y. (2025). CryptoTensors: A Light-Weight Large Language Model File Format for Highly-Secure Model Distribution. arXiv:2512.04580. https://arxiv.org/pdf/2512.04580

License

Apache-2.0 License