Data Poisoning

Data poisoning is an attack in which malicious examples are injected into a model's training data to corrupt the model's behavior in targeted or general ways. A poisoned model may behave normally on most inputs but produce attacker-controlled outputs when a specific trigger phrase appears — a backdoor attack. Defenses include dataset provenance tracking, anomaly detection during training data curation, and robustness evaluation that tests for trigger-based behavior shifts in the trained model.