# pipeline-security
標記為「pipeline-security」的 9 篇文章
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.
CI/CD 管線 AI 風險
將 AI 整合至 CI/CD 管線的安全意涵——涵蓋建構中的 AI 驅動程式碼生成、自動化測試風險、部署決策操控與管線強化。
Secure RAG Pipeline Design Patterns
安全-first design patterns for RAG pipelines including source validation, content sanitization, and retrieval controls.
攻擊ing ML CI/CD Pipelines
進階 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 安全
ML 持續整合與部署管線的安全概觀：ML CI/CD 與傳統 CI/CD 的差異、訓練工作流程中的獨特攻擊面，以及自動化模型建構與部署的安全意涵。