# experiment-tracking
6 articlestagged with “experiment-tracking”
Attacking Experiment Tracking Systems
Techniques for exploiting experiment tracking platforms like MLflow, Weights & Biases, Neptune, and CometML, including data exfiltration, metric manipulation, experiment injection, and leveraging tracking metadata for reconnaissance.
MLflow Security Hardening
Securing MLflow deployments against unauthorized access, experiment tampering, and model registry poisoning.
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.
Experiment Metadata Leakage
How experiment metadata reveals sensitive information: hyperparameters exposing architecture secrets, loss curves revealing training data properties, run names and tags disclosing project intent, and techniques for extracting intelligence from ML experiment logs.
Weights & Biases Attack Surface
Security analysis of Weights & Biases (W&B/wandb): API key exposure, experiment data leakage, team boundary violations, artifact poisoning, and attack techniques specific to the W&B platform.
ML Experiment Tracking Security
Securing experiment tracking systems like MLflow, Weights & Biases, and Neptune.