PrivacyRaven is a privacy testing library for deep learning systems. You can use it to determine the susceptibility of a model to different privacy attacks; evaluate privacy preserving machine learning techniques; develop novel privacy metrics and attacks; and repurpose attacks for data provenance and other use cases. PrivacyRaven supports label-only black-box model extraction, membership inference, and (soon) model inversion attacks. We also plan to include differential privacy verification, automated hyperparameter optimization, more classes of attacks, and other features; see the GitHub issues for more information. PrivacyRaven has been featured at the OpenMined Privacy Conference, Empire Hacking, and Trail of Bits blog.