Popular GitHub Repositories for Blackbox Attack

Fit interpretable models. Explain blackbox machine learning.

Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference - Red and Blue Teams

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

Master Federated Learning in 2 Hours—Run It on Your PC!

Python-based research interface for blackbox and hyperparameter optimization, based on the internal Google Vizier Service.

Open-source implementation of Google Vizier for hyper parameters tuning

Must-read Papers on Textual Adversarial Attack and Defense

Quark Agent - Your AI-powered Android APK Analyst

Keras implementation of "One pixel attack for fooling deep neural networks" using differential evolution on Cifar10 and ImageNet

It's not AI that takes away your job, but the people who master the use of AI tools. The most deadly attack is a dimension-reducing strike: destroying you has nothing to do with you - from "The Three-Body Problem". 中文说明： 抢走你工作的不是AI，而是掌握使用AI工具的人。 降维打击最为致命：毁灭你，与你何干《三体》

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。

集成了openai-api、coze、deepseek、cursor、windsurf、qodo、blackbox、you、grok、bing 绘画 多款AI的聊天逆向接口适配到 OpenAI API 标准接口服务端。

A curated list of adversarial attacks and defenses papers on graph-structured data.

An Open-Source Package for Textual Adversarial Attack.

Privacy Meter: An open-source library to audit data privacy in statistical and machine learning algorithms.

A Model for Natural Language Attack on Text Classification and Inference

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

A script for credentials-based attack surface enumeration and general reconnaissance of massive networks

Explainable AI framework for data scientists. Explain & debug any blackbox machine learning model with a single line of code. We are looking for co-authors to take this project forward. Reach out @ ms8909@nyu.edu