No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by RiccardoBiosas · MCP Server · ★ 465
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is awesome-MLSecOps safe to install? View the security audit →
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 | 465 |
| Forks | 103 |
| Language | Astro |
| Category | MCP Server |
| License | MIT |
| Quality Score | 59.3497523882864/100 |
| Open Issues | 14 |
| Last Updated | 2026-09-09 |
| Created | 2023-04-01 |
| Platforms | mcp |
| Est. Tokens | ~25k |
These tools work well together with awesome-MLSecOps for enhanced workflows:
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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 465 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 465 stars and 103 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.
The top alternatives to awesome-MLSecOps on Agent Skills Hub include Adrian, agent-opfor, www-project-agent-memory-guard. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: