by RiccardoBiosas · MCP Server · ★ 451
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.
| Stars | 451 |
| Forks | 91 |
| Language | Astro |
| Category | MCP Server |
| License | MIT |
| Quality Score | 60.2072564398796/100 |
| Open Issues | 13 |
| Last Updated | 2026-08-15 |
| 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 451 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 451 stars and 91 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.