What does a training-data poisoning attack do?

Why this is the answer

Poisoning attacks the learning phase: adversarial examples introduced into the training set become part of what the model believes, and the damage is baked in until retraining. Manipulating a single inference is evasion, swapping stored weights is artifact tampering, and corrupting only the evaluation set misreports quality without changing behaviour.