# wandb
標記為「wandb」的 8 篇文章
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.
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.
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.
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.
攻擊ing 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.
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.
實驗追蹤安全
ML 實驗追蹤系統中的安全風險：會被記錄什麼、哪些是敏感內容，以及追蹤平台為何成為攻擊者尋求智財與管線存取的高價值目標。
Weights & Biases 攻擊面
Weights & Biases（W&B／wandb）之安全分析：API 金鑰曝露、實驗資料洩漏、團隊邊界越界、產物投毒，以及 W&B 平台特有之攻擊技術。