Methodologies and practices for security testing of systems based on artificial intelligence, machine learning, and deep learning. Includes adversarial testing, robustness evaluation, prompt injection testing, model extraction attempts, data poisoning detection, privacy leakage assessment, and resilience verification against AI-specific attacks like membership inference, model inversion, and backdoor injection.