Shipped: Adversarial AI — Compromised Models and Attack Datasets

An open-source collection of adversarial AI research artifacts developed for Book 2 of The Applied AI Universe Coding Guide series.

The repository contains deliberately compromised machine learning models and datasets used to demonstrate how adversarial attacks can undermine AI systems.

Research artifacts include poisoned training datasets, corrupted model parameters, manipulated symbolic AI components, and backdoored LoRA adapters.

These examples support the study of adversarial machine learning, data poisoning, model integrity, backdoor vulnerabilities, and AI security evaluation.

The collection is designed to work alongside the clean baseline models from Book 1 and the corresponding defensive experiments in Book 3, allowing researchers and educators to examine the complete progression from model development to attack and defense.

IMPORTANT: These artifacts are intentionally compromised and must not be deployed in production or loaded into trusted systems. They are intended for authorized research and controlled educational environments.