by RiccardoBiosas · MCP Server · ★ 440
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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 or read the contribution guidelines to suggest a resource. Table of Contents [What is MLSecOps?](#what-
| Stars | 440 |
| Forks | 88 |
| Language | Astro |
| Category | MCP Server |
| License | MIT |
| Quality Score | 60.2072564398796/100 |
| Open Issues | 7 |
| Last Updated | 2026-07-24 |
| Created | 2023-04-01 |
| Platforms | mcp |
| Est. Tokens | ~25k |
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awesome-MLSecOps is 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 p. It is categorized as a MCP Server with 440 GitHub stars.
awesome-MLSecOps is primarily written in Astro. It covers topics such as adversarial-machine-learning, agentic-security, ai-agents.
You can find installation instructions and usage details in the awesome-MLSecOps GitHub repository at github.com/RiccardoBiosas/awesome-MLSecOps. The project has 440 stars and 88 forks, indicating an active community.
awesome-MLSecOps is released under the MIT license, making it free to use and modify according to the license terms.
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