Popular GitHub Repositories for Injection Attacks

A collection of GPT system prompts and various prompt injection/leaking knowledge.

An adversarial example library for constructing attacks, building defenses, and benchmarking both

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

ChatGPT Jailbreaks, GPT Assistants Prompt Leaks, GPTs Prompt Injection, LLM Prompt Security, Super Prompts, Prompt Hack, Prompt Security, Ai Prompt Engineering, Adversarial Machine Learning.

A unified evaluation framework for large language models

PyTorch implementation of adversarial attacks [torchattacks]

The Security Toolkit for LLM Interactions

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

My personal prompt library for various LLMs + scripts & tools. Suitable for models from Deepseek, OpenAI, Claude, Meta, Mistral, Google, Grok, and others.

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

LLM Prompt Injection Detector

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

? LangKit: An open-source toolkit for monitoring Large Language Models (LLMs). ? Extracts signals from prompts & responses, ensuring safety & security. ?? Features include text quality, relevance metrics, & sentiment analysis. ? A comprehensive tool for LLM observability. ?

a prompt injection scanner for custom LLM applications

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 Convolutional Network.