Popular GitHub Repositories for Proactive Defense

FedML - The Research and Production Integrated Federated Learning Library: https://fedml.ai

Must-read Papers on Textual Adversarial Attack and Defense

A pytorch adversarial library for attack and defense methods on images and graphs

Chinese safety prompts for evaluating and improving the safety of LLMs. 中文安全prompts，用于评估和提升大模型的安全性。

Sentiment Analysis, Text Classification, Text Augmentation, Text Adversarial defense, etc.;

Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities. arXiv:2408.07666.

Adversarial attacks and defenses on Graph Neural Networks.

? Adversarial attacks on explanations and how to defend them

auto_LiRPA: An Automatic Linear Relaxation based Perturbation Analysis Library for Neural Networks and General Computational Graphs

Implementation of the KDD 2020 paper "Graph Structure Learning for Robust Graph Neural Networks"

Agentic AI platform that harnesses Visual LLM Chaining to build proactive digital assistants

Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models (published in ICLR2018)

Code for our NeurIPS 2019 *spotlight* "Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers"

A certifiable defense against adversarial examples by training neural networks to be provably robust

A curated list of papers on adversarial machine learning (adversarial examples and defense methods).

prompt attack-defense, prompt Injection, reverse engineering notes and examples | 提示词对抗、破解例子与笔记

A survey on harmful fine-tuning attack for large language model

Official TensorFlow Implementation of Adversarial Training for Free! which trains robust models at no extra cost compared to natural training.

The goal of this survey is two-fold: (i) to present recent advances on adversarial machine learning (AML) for the security of RS (i.e., attacking and defense recommendation models), (ii) to show another successful application of AML in generative adversarial networks (GANs) for generative applications, thanks to their ability for learning (high-dimensional) data distributions. In this survey, we provide an exhaustive literature review of 74 articles published in major RS and ML journals and conferences. This review serves as a reference for the RS community, working on the security of RS or on generative models using GANs to improve their quality.

A guide to LLM hacking: fundamentals, prompt injection, offense, and defense