The Machine Learning Security Atlas
Awesome MLSecOps — Curated ML & AI Security Tools and Resources
MLSecOps (Machine Learning Security Operations) integrates security engineering, threat modeling, testing, supply-chain controls, monitoring, and incident response across the machine-learning lifecycle. It protects data, models, pipelines, infrastructure, LLM applications, and AI agents against poisoning, adversarial manipulation, unsafe artifacts, privacy leakage, model extraction, prompt injection, and excessive agency.
Browse by security problem
Focused guides, one community source
Browse MLSecOps tools and resources by machine learning security category. The GitHub repository contains the complete, community-maintained catalog.
LLM Security and Red Teaming
Test prompts, model behavior, guardrails, and application controls against abuse.
15 catalog entriesModel Scanning and Validation
Inspect model files, notebooks, code, and behavior before release or deployment.
21 catalog entriesAdversarial Machine Learning
Evaluate evasion, poisoning, extraction, inversion, and model robustness.
6 catalog entriesAI Supply-Chain Security
Protect model provenance, artifacts, dependencies, signing, and delivery pipelines.
18 catalog entriesAI Agent and MCP Security
Secure agent tools, memory, identity, permissions, sandboxes, and MCP servers.
12 catalog entriesPrivacy-Preserving Machine Learning
Reduce sensitive-data exposure and test privacy leakage in ML systems.
Newsletter
Latest from The MLSecOps Hacker
Articles on MLSecOps tooling, AI supply-chain security, prompt injection, and agent security, published first on the maintainer's newsletter.
Research roundup #1: Rogue Agents, Provenance Engineering and Vulnerable Agent Skills
The AI Security threats and research areas I am researching at the end of this quarter and why
Safetensors Won. Model Serialization Attacks Didn’t Stop.
Safetensors became the default model format, yet serialization attacks moved into conversion pipelines, loaders, and unsafe infrastructure.
AI Security: Model Serialization Attacks
A practical review of model serialization risks, machine learning supply-chain vulnerabilities, and defensive practices for handling model artifacts safely.
What is MLSecOps?
An introduction to the discipline of securing machine learning systems and the teams, controls, and operating practices that support it.