Data Poisoning
Deliberately corrupting a model’s training data so it learns a hidden backdoor or a biased, attacker-chosen behavior.
Data poisoning attacks the supply chain of the model itself rather than its runtime behavior: an attacker who can influence training data (scraped web text, a fine-tuning dataset, human feedback labels) inserts examples designed to teach the model a specific hidden trigger-response pattern, or to subtly bias it. It’s harder to detect than a runtime jailbreak because the compromised behavior looks like ordinary, if slightly unusual, model output until the trigger appears.