# pipeline-security
5 articlestagged with “pipeline-security”
LLMOps Security Assessment (Assessment)
Test your understanding of MLOps pipeline security, model deployment attacks, API security, monitoring gaps, model registry poisoning, and CI/CD for ML with 10 questions.
CI/CD Pipeline AI Risks
Security implications of integrating AI into CI/CD pipelines — covering AI-powered code generation in builds, automated testing risks, deployment decision manipulation, and pipeline hardening.
Secure RAG Pipeline Design Patterns
Security-first design patterns for RAG pipelines including source validation, content sanitization, and retrieval controls.
Attacking ML CI/CD Pipelines
Advanced techniques for compromising ML continuous integration and deployment pipelines, including pipeline injection, artifact tampering, training job hijacking, and exploiting the unique trust boundaries in automated ML workflows.
ML CI/CD Security
Security overview of ML continuous integration and deployment pipelines: how ML CI/CD differs from traditional CI/CD, unique attack surfaces in training workflows, and the security implications of automated model building and deployment.