Awesome Machine Unlearning (A Survey of Machine Unlearning)

[NeurIPS D&B '25] The one-stop repository for large language model (LLM) unlearning. Supports TOFU, MUSE, WMDP, and many unlearning methods with easily feature extensibility.

Security and Privacy Risk Simulator for Machine Learning (arXiv:2312.17667)

Privacy Testing for Deep Learning

Python package for measuring memorization in LLMs.

[ICLR24 (Spotlight)] "SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation" by Chongyu Fan*, Jiancheng Liu*, Yihua Zhang, Eric Wong, Dennis Wei, Sijia Liu

[NeurIPS23 (Spotlight)] "Model Sparsity Can Simplify Machine Unlearning" by Jinghan Jia*, Jiancheng Liu*, Parikshit Ram, Yuguang Yao, Gaowen Liu, Yang Liu, Pranay Sharma, Sijia Liu

What does gpt-oss tell us about OpenAI's training data?

reveal the vulnerabilities of SplitNN

A unified evaluation suite for membership inference and machine text detection.

A repository about literature of copyright protection in deep learning.

Source code for EMNLP 2024 Findings paper: Code Membership Inference for Detecting Unauthorized Data Use in Code Pre-trained Language Models.

Model extraction attack — exploratory implementation and analysis for learning purposes

Experiments at the intersection of ML security & privacy: adversarial attacks/defenses (FGSM/PGD, adversarial training), differential privacy (DP-SGD, ε–δ), federated learning privacy (secure aggregation), and auditing (membership/model inversion). PyTorch notebooks + eval scripts.