The most effective attacks against artificial intelligence (AI) systems exploit mathematical properties inherent to machine learning algorithms. Adversarial examples—inputs crafted to cause misclassification—reveal fundamental vulnerabilities in neural networks that defenders must understand to build robust systems. These carefully constructed inputs appear normal to human observers but cause AI models to produce dramatically incorrect outputs with high confidence.