Evaluation of AI/ML model resilience against poisoning attacks during execution, where malicious inputs or manipulated feedback alter model behavior in production. Includes tests for backdoor injection, incremental data poisoning, adversarial retraining, and compromise of continuous learning pipelines in operational environments.