Data Poisoning

Data Poisoning is a type of adversarial attack where malicious data is intentionally introduced into a model's training dataset. This corrupted data can compromise the model's integrity, leading it to learn incorrect patterns or biases. It's like a saboteur secretly adding bad ingredients to a recipe, making the final dish unreliable or harmful in production.