Data Poisoning

Data poisoning is an attack that corrupts a model's training or retrieval data so that its behavior is subtly manipulated at inference time. In systems like Vis-Poison, a single carefully edited image inserted into a retrieval corpus can be enough to mislead every downstream answer that draws on it, showing how small, targeted corruptions can undermine trust in retrieval-augmented pipelines far out of proportion to their size.