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Adversary Emulation Framework

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

Data augmentation for NLP

TextAttack 🐙 is a Python framework for adversarial attacks, data augmentation, and model training in NLP https://textattack.readthedocs.io/en/master/

A Python toolbox to create adversarial examples that fool neural networks in PyTorch, TensorFlow, and JAX

A unified evaluation framework for large language models

PyTorch implementation of adversarial attacks [torchattacks]

A reading list for large models safety, security, and privacy (including Awesome LLM Security, Safety, etc.).

Must-read Papers on Textual Adversarial Attack and Defense

Advbox is a toolbox to generate adversarial examples that fool neural networks in PaddlePaddle、PyTorch、Caffe2、MxNet、Keras、TensorFlow and Advbox can benchmark the robustness of machine learning models. Advbox give a command line tool to generate adversarial examples with Zero-Coding.

A Toolbox for Adversarial Robustness Research

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

This repository contains detailed adversary simulation APT campaigns targeting various critical sectors. Each simulation includes custom tools, C2 servers, backdoors, exploitation techniques, stagers, bootloaders, and other malicious artifacts that mirror those used in real world attacks .

A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convol…

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

Internal Safety Collapse: Turning the LLM or an AI Agent into a sensitive data generator.

An Open-Source Package for Textual Adversarial Attack.

Code relative to "Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks"

Raising the Cost of Malicious AI-Powered Image Editing