backdoor-federated-learning

A list of backdoor learning resources

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.

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

A curated list of MLSecOps tools, articles and other resources on security applied to Machine Learning and MLOps systems.

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

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.

A curated list of academic events on AI Security & Privacy

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.

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

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