Translation: Data poisoning is a type of attack where someone changes, corrupts, or adds misleading data to a training dataset to influence how a machine learning model behaves. Because models have patterns based on their training data, poisoned data can cause them to make wrong, unsafe, or biased predictions. Data poisoning is the deliberate tampering of training data designed to harm or otherwise change a model’s performance.