 A large language model uses deep neural networks to generate outputs based on patterns learned from training data.  Training data poisoning refers to manipulating the data or fine-tuning process to introduce vulnerabilities, backdoors or biases that could compromise the model's security, effectiveness or ethical behavior.  Poisoned information may be surfaced to users or create other risks like performance degradation, downstream software exploitation and reputational damage.  Even if users distrust the problematic AI output, the risks remain, including impaired model capabilities and potential harm to brand reputation.  Data poisoning is considered an integrity attack because tampering with the training data impacts the model's ability to output correct predictions