exploitai 0.1.0

AI Model Security Testing Framework for research and education

ExploitAI

AI Model Security Testing Framework for research and education.

Overview

ExploitAI is a Python library designed to demonstrate and test security vulnerabilities in machine learning models. It focuses on:

• Gradient Inversion Attacks (DLG, iDLG, GIAS)
• Membership Inference Attacks (Shokri et al., variations)
• Model Poisoning Attacks (data poisoning, Byzantine attacks)

Installation

pip install exploitai

Quick Start

import exploitai as ea

# Load a pre-trained model
model = ea.models.load_model("resnet18", pretrained=True)

# Run gradient inversion attack
attack = ea.GradientInversion(method="idlg")
results = attack.run(model, target_gradients)

# Generate security report
ea.generate_report(results, output="security_audit.html")

Purpose

This library is developed for:

• Security Research: Understanding ML model vulnerabilities
• Educational Use: Teaching AI security concepts
• Defensive Testing: Evaluating model robustness

Note: This tool is intended for defensive security research and education only.

Development Status

🚧 Alpha Release - Core framework and basic attacks in development

License

MIT License - see LICENSE file for details.