Image Payload Creating/Injecting tools

A list of backdoor learning resources

For educational purposes only, samples of 400+ classic/modern trojan builders including screenshots.

a unique framework for cybersecurity simulation and red teaming operations, windows auditing for newer vulnerabilities, misconfigurations and privilege escalations attacks, replicate the tactics and techniques of an advanced adversary in a network.

The open-sourced Python toolbox for backdoor attacks and defenses.

Hide your payload into .jpg file

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.

Code implementation of the paper "Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks", at IEEE Security and Privacy 2019.

A curated list of papers & resources linked to data poisoning, backdoor attacks and defenses against them (no longer maintained)

A curated list of papers & resources on backdoor attacks and defenses in deep learning.

An open-source toolkit for textual backdoor attack and defense (NeurIPS 2022 D&B, Spotlight)

This is an implementation demo of the ICLR 2021 paper [Neural Attention Distillation: Erasing Backdoor Triggers from Deep Neural Networks](https://openreview.net/pdf?id=9l0K4OM-oXE) in PyTorch.

WaNet - Imperceptible Warping-based Backdoor Attack (ICLR 2021)

Persistent Powershell backdoor tool {😈}

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.

BackdoorSim: An Educational into Remote Administration Tools

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

Codes for NeurIPS 2021 paper "Adversarial Neuron Pruning Purifies Backdoored Deep Models"

[ICCV 2023] Source code for our paper "Rickrolling the Artist: Injecting Invisible Backdoors into Text-Guided Image Generation Models".