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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
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.
| Date | Stars |
|---|---|
| 2026-07-31 | 442 |
| 2026-08-04 | 443 |
| 2026-08-06 | 444 |
Today
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# Awesome MLSecOps: Machine Learning and AI Security Resources 🛡️🤖 ## What is MLSecOps? **MLSecOps (Machine Learning Security Operations)** is the practice of integrating security throughout the machine learning lifecycle—from data collection and model development to deployment, monitoring, and incident response. It applies security testing, threat modeling, supply-chain protection, access controls, and continuous monitoring to machine learning models, MLOps pipelines, LLM applications, and AI agents. This curated catalog helps security engineers, ML practitioners, developers, and AI red teams discover open-source MLSecOps tools, adversarial machine learning research, AI security frameworks, threat-modeling resources, and practical learning materials. ⭐ If this catalog is useful, [star the repository](https://github.com/RiccardoBiosas/awesome-MLSecOps) or read the [contribution guidelines](CONTRIBUTING.md) to suggest a resource. [](https://awesome.re) [](https://github.com/RiccardoBiosas/awesome-MLSecOps/graphs/commit-activity)  [](https://twitter.com/RBiosas)  ## Table of Contents - [What is MLSecOps?](#what-is-mlsecops) - [Open Source Security Tools](#open-source-security-tools) - [Commercial MLSecOps and AI Security Tools](#commercial-mlsecops-and-ai-security-tools) - [Data Privacy and Anonymization Tools](#data-privacy-and-anonymization-tools) - [ML Code Security](#ml-code-security) - [AI Security and MLSecOps Beginner Resources](#ai-security-and-mlsecops-beginner-resources) - [Threat Modeling](#threat-modeling) - [Attack Vectors](#attack-vectors) - [Blogs and Publications](#blogs-and-publications) - [MLOps Infrastructure Vulnerabilities](#mlops-infrastructure-vulnerabilities) - [MLSecOps Pipeline](#mlsecops-pipeline) - [Adversarial Machine Learning Research Repositories](#adversarial-machine-learning-research-repositories) - [Community Resources](#community-resources) - [Books](#books) - [Infographics](#infographics) - [Contributions](#contributions) - [Contributors](#contributors-) - [Repository Stats](#repository-stats) - [Activity](#activity) - [Support Us](#support-us) - [License](#license) ## Open Source Security Tools Open-source MLSecOps tools help practitioners test, monitor, and protect machine learning models, data, pipelines, LLM applications, and AI agents. The resources below include active projects and proofs of concept; evaluate maintenance status, licensing, threat coverage, and deployment suitability before production use. ### Model and Artifact Security Model and artifact security tools detect unsafe serialization, malicious payloads, integrity failures, and other risks in machine learning model files. | Tool | Description | |------|-------------| | [ModelScan](https://github.com/protectai/modelscan) | Protection Against ML Model Serialization Attacks | | [Safetensors](https://github.com/huggingface/safetensors) | Tensor storage format designed to avoid executable deserialization; it does not establish model provenance or behavioral safety | ### Adversarial Machine Learning Testing Adversarial machine learning testing tools evaluate model robustness against evasion, poisoning, extraction, inversion, and other adversarial techniques. | Tool | Description | |------|-------------| | [Adversarial Robustness Toolbox](https://github.com/IBM/adversarial-robustness-toolbox) | Library of defense methods for ML models against adversarial attacks | | [Foolbox](https://github.com/bethgelab/foolbox) | Python toolbox for creating and evaluating adversarial attacks and defenses | ### LLM Security and Red Teaming LLM security and red-teaming t
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:37c5601957496129, topic:ai-security, topic:prompt-injection, topic:llm-security