The user wants to understand Data Poisoning in the context of LLM Security and apply it to practical AI governance or compliance work.
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.
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