pralab/secml

Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference - Red and Blue Teams

Interpretability and explainability of data and machine learning models

Model interpretability and understanding for PyTorch

A game theoretic approach to explain the output of any machine learning model.

A collection of real world AI/ML exploits for responsibly disclosed vulnerabilities

🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models

A curated list of MLSecOps tools and resources for securing machine learning and AI systems - adversarial ML defense, LLM security, AI red teaming, model scanning, supply-chain protection, and MLOps pipeline security.

Library for training machine learning models with privacy for training data