Popular GitHub Repositories for Backdoor Defenses

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

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

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

Adversarial attacks and defenses on Graph Neural Networks.

A curated list of trustworthy deep learning papers. Daily updating...

Backdoors Framework for Deep Learning and Federated Learning. A light-weight tool to conduct your research on backdoors.

TrojanZoo provides a universal pytorch platform to conduct security researches (especially backdoor attacks/defenses) of image classification in deep learning.

This repository provides implementation to formalize and benchmark Prompt Injection attacks and defenses

A Dynamic Environment to Evaluate Attacks and Defenses for LLM Agents.

DEEPSEC: A Uniform Platform for Security Analysis of Deep Learning Model

BackdoorLLM: A Comprehensive Benchmark for Backdoor Attacks on Large Language Models

Experimental tools to backdoor large language models by re-writing their system prompts at a raw parameter level. This allows you to potentially execute offline remote code execution without running any actual code on the victim's machine or thwart LLM-based fraud/moderation systems.

?? Blades: A Unified Benchmark Suite for Attacks and Defenses in Federated Learning

[NeurIPS 2024] Official implementation for "AgentPoison: Red-teaming LLM Agents via Memory or Knowledge Base Backdoor Poisoning"

Code for the CVPR 2019 article "Decoupling Direction and Norm for Efficient Gradient-Based L2 Adversarial Attacks and Defenses"

The official implementation of the CCS'23 paper, Narcissus clean-label backdoor attack -- only takes THREE images to poison a face recognition dataset in a clean-label way and achieves a 99.89% attack success rate.

PyInstaCrack: Ultimate Instagram hacking suite. Python-driven, AI-enhanced, brute-force chaos. Stealth ops, ethical only. Slice through defenses like a cyber god! ⚡☠️

A benchmark for evaluating the robustness of LLMs and defenses to indirect prompt injection attacks.

CRFL: Certifiably Robust Federated Learning against Backdoor Attacks (ICML 2021)

ICML 2022 code for "Neurotoxin: Durable Backdoors in Federated Learning" https://arxiv.org/abs/2206.10341