Most production AI models share a fundamental flaw: their decision boundaries are dangerously brittle. A single pixel change can flip a cat classifier into a guacamole recipe, exposing vulnerabilities in systems from spam filters to self-driving cars. This fragility stems from how machine learning models learn patterns from data rather than understanding concepts.

Attackers exploit these boundaries through adversarial examples—imperceptible noise that fools models—and data poisoning, where malicious training examples create backdoors. These attacks work because models lack true comprehension; they only know which side of a mathematical surface an input falls on. Traditional software security doesn't apply here.

Defense requires treating models as untrusted components. Adversarial training exposes models to attacks during training, while ensemble methods use multiple architectures to resist compromise. For critical systems like medical diagnosis or autonomous vehicles, these measures aren't optional—they're requirements as attackers grow more creative with geometric exploits.