🎯 Overview

NullSec Adversarial is a comprehensive toolkit for testing machine learning model robustness. It implements state-of-the-art adversarial attacks — evasion (FGSM, PGD, C&W, AutoAttack), model extraction, membership inference, and model inversion — across image classifiers, NLP models, and tabular ML pipelines.

⚡ Features

�� Attack Matrix

🚀 Quick Start

# Run PGD evasion attack on an image classifier
nullsec-adversarial evasion pgd --model resnet50.onnx --input samples/ --eps 0.03

# Black-box model extraction
nullsec-adversarial extract --target-url http://api.example.com/predict --queries 10000

# Membership inference attack
nullsec-adversarial membership --model target.pt --members train.csv --non-members test.csv

# Evaluate adversarial robustness
nullsec-adversarial benchmark --model model.pt --dataset cifar10 --attacks all

🔗 Related Projects

⚠️ Legal

For authorized ML security testing only. Do not use against models or systems without explicit permission.

📜 License

MIT License — @bad-antics