Training data poisoning refers to manipulation of pre-training data or data involved within the fine-tuning or embedding processes to introduce vulnerabilities (which all have unique and sometimes shared attack vectors), 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.