Also known as: Poisoning
Data poisoning is an adversarial attack in which a malicious actor interferes with the data or training process used to build a model. By inserting, modifying, or corrupting training examples, the attacker seeks to manipulate model behavior, degrade performance, or create targeted failures.
βAdversarial attacks in which an adversary interferes with a model during its training stage, such as by inserting malicious training data (data poisoning) or modifying the training process itself (model poisoning).β
A side-by-side comparison of Prompt Injection and Data Poisoning. Understand how runtime instruction manipulation differs from attacks on training data or the training process.
A side-by-side comparison of Data Poisoning and Adversarial Learning. Understand how the terms differ, when each applies, and what the distinction means for AI governance, system design, or assurance evidence.