# model-registry
標記為「model-registry」的 15 篇文章
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.
Azure ML 利用ation
Red team attack methodology for Azure Machine Learning: workspace security, compute instance attacks, pipeline poisoning, model registry tampering, and data store exploitation.
Cloud 模型 Registry 安全
安全 of cloud model registries including SageMaker 模型 Registry, Azure ML Registry, and Vertex AI 模型 Registry.
投毒 模型 Registries
進階 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.
模型 Registry 安全
Securing model registries and artifact stores against tampering, poisoning, and unauthorized access.
模型供應鏈
AI 模型供應鏈中的安全風險——涵蓋模型登錄攻擊、序列化利用、依賴漏洞與模型完整性驗證。
MLflow 攻擊 Surface
安全 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.
模型登錄安全（LLMOps 安全）
模型登錄之安全概觀：登錄如何管理模型生命週期、存取控制模型、信任邊界，以及儲存與散發不透明 ML 產物的獨特安全挑戰。
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.