This repository is a curated list of open-source tools, articles, and resources focused on Machine Learning Security Operations (MLSecOps). It aims to provide a comprehensive overview of security applied to Machine Learning and MLOps systems, targeting security professionals, ML engineers, and researchers interested in understanding and mitigating AI-specific threats.

How It Works

The list is organized into categories such as Open Source Security Tools, Commercial Tools, Data Security, ML Code Security, Attack Vectors, and community resources. It highlights specific tools for tasks like vulnerability scanning (e.g., Garak, Vigil), adversarial attack generation (e.g., TextAttack, Foolbox), privacy preservation (e.g., TensorFlow Privacy, OpenDP), and data anonymization (e.g., ARX, DeepPrivacy2). The collection also includes links to foundational knowledge, threat models, and ongoing research.

Quick Start & Requirements

This is a curated list, not a runnable application. No installation or specific requirements are needed to browse the resources. Links to official documentation, demos, or GitHub repositories are provided for individual tools.

Highlighted Details

• Extensive catalog of open-source tools for ML security, covering adversarial attacks, data privacy, and vulnerability scanning.
• Includes resources on ML code security, attack vectors, and MLOps infrastructure vulnerabilities.
• Features links to academic papers, blogs, podcasts, and community channels for continuous learning.
• Covers both offensive (attack generation) and defensive (mitigation, privacy) aspects of ML security.

Maintenance & Community

The project is maintained by @riccardobiosas and several contributors. It encourages community contributions via pull requests. Links to relevant Slack communities, podcasts, and organizations like MITRE and OWASP are provided.

Licensing & Compatibility

The repository itself is licensed under the MIT License. Individual tools listed within the repository will have their own licenses, which may vary and could include restrictions on commercial use or linking.

Limitations & Caveats

The README notes that some listed open-source tools may be unsupported or difficult to run. It is a curated list, so the effectiveness and maintenance status of each individual tool require separate evaluation.