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🎯 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

Feature	Description
Evasion Attacks	FGSM, PGD, C&W, DeepFool, AutoAttack
Model Extraction	Query-based model stealing with knockoff networks
Membership Inference	Determine if a sample was in training data
Model Inversion	Reconstruct training data from model outputs
Transferability	Generate transferable adversarial examples
Defence Evaluation	Test adversarial training, certified defences
Framework Support	PyTorch, TensorFlow, scikit-learn, ONNX

�� Attack Matrix

Attack	Type	Domain	Threat Model
FGSM	Evasion	Image/NLP	White-box
PGD	Evasion	Image/NLP	White-box
C&W	Evasion	Image	White-box
AutoAttack	Evasion	Image	White-box
HopSkipJump	Evasion	Image	Black-box
Knockoff Nets	Extraction	Any	Black-box
Shadow Models	Membership	Any	Black-box
MI-FACE	Inversion	Image	White-box

🚀 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

Project	Description
nullsec-llmred	LLM red-teaming framework
nullsec-datapoisoning	Training data poisoning detection
nullsec-modelaudit	ML model security auditing
nullsec-promptinject	Prompt injection payloads
nullsec-linux	Security Linux distro (140+ tools)

⚠️ Legal

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

📜 License

MIT License — @bad-antics

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