Popular GitHub Repositories for Poisoning Defenses

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

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"

This repository contains the code to generate predictions of critical violations at food establishments in Chicago. It also contains the results of an evaluation of the effectiveness of those predictions.

Security and Privacy Risk Simulator for Machine Learning (arXiv:2312.17667)

Adversarial attacks and defenses on Graph Neural Networks.

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

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.

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

A Python library for Secure and Explainable Machine Learning

A curated list of academic events on AI Security & Privacy

?? 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! ⚡☠️