AI Model and Training Data Exfiltration via Adversarial API Attacks

Executive Summary

Adversaries can exploit public-facing model APIs to extract private training data, including sensitive medical records, and steal proprietary model architecture through membership inference and model distillation attacks. Without targeted mitigations, organisations face simultaneous breaches of data protection law and loss of core AI intellectual property.