# ml-security
5 articlestagged with “ml-security”
Quantum Computing Implications for ML Security
Analysis of how quantum computing advances affect ML model security, extraction, and adversarial robustness.
Experiment Tracking Security
Security risks in ML experiment tracking systems: what gets logged, what is sensitive, and how tracking platforms become high-value targets for attackers seeking intellectual property and pipeline access.
LLMOps Security
Comprehensive overview of security across the LLMOps lifecycle: from data preparation and experiment tracking through model deployment and production monitoring. Attack surfaces, threat models, and defensive strategies for ML operations.
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.
Counterfit ML Security Testing
Use Microsoft's Counterfit for adversarial ML testing of deployed model endpoints.