# model-registry
7 articlestagged with “model-registry”
Azure ML Exploitation
Red team attack methodology for Azure Machine Learning: workspace security, compute instance attacks, pipeline poisoning, model registry tampering, and data store exploitation.
Cloud Model Registry Security
Security of cloud model registries including SageMaker Model Registry, Azure ML Registry, and Vertex AI Model Registry.
Poisoning Model Registries
Advanced techniques for attacking model registries like MLflow, Weights & Biases, and Hugging Face Hub, including model replacement attacks, metadata manipulation, artifact poisoning, and supply chain compromise through registry infrastructure.
Model Registry Security
Securing model registries and artifact stores against tampering, poisoning, and unauthorized access.
MLflow Attack Surface
Security analysis of MLflow: tracking server authentication weaknesses, artifact store access control, model registry tampering, SQL injection in tracking queries, and exploitation techniques for both open-source and managed deployments.
Model Registry Security (Llmops Security)
Security overview of model registries: how registries manage model lifecycle, access control models, trust boundaries, and the unique security challenges of storing and distributing opaque ML artifacts.
Databricks MLflow Deployment Audit
End-to-end walkthrough for auditing MLflow deployments on Databricks: workspace enumeration, model registry security, serving endpoint testing, Unity Catalog integration review, and audit log analysis.