Model inversion is an attack method targeting AI models, where an attacker infers information about the model's training data by analyzing the model's output. It effectively “reverse-engineers” the model to uncover the data it was trained on, which can lead to exposure of sensitive or private information.

‍Why it matters: Model inversion can expose private or proprietary data, posing serious risks to privacy, security, and compliance.