Training data poisoning is an AI model supply chain attack where malicious or manipulated data is introduced into the training dataset to compromise model behavior. Testing verifies if an AI system was trained on contaminated data causing intentional bias, backdoors, manipulated outputs on specific triggers, or performance degradation on certain inputs. Includes data provenance analysis, anomalous pattern detection, and validation of model robustness against corrupted datasets.