Introduction and Application of Model Hacking

This blog explains adversarial machine learning (“model hacking”) and demonstrates how AI models can be evaded or poisoned, including digital attacks against Android malware classifiers and physical perturbations of traffic signs that could affect autonomous driving systems. It highlights transferability of adversarial examples across models, shows how small feature changes can cause misclassification, and reviews detection/mitigation approaches such as monitoring model drift, XAI, and defenses like feature squeezing and ensemble methods. The piece positions the research as proactive threat readiness rather than documenting real-world incidents.