Data poisoning attacks in machine learning systems: intentional manipulation of training datasets to compromise model integrity, accuracy, or behavior, including malicious sample insertion, backdoor injection, label flipping, poisoning techniques aimed at causing misclassifications, evasion, or performance degradation, and defenses based on data sanitization, anomaly detection, and robust training.