Data Poisoning

AI Red Teamers simulate data poisoning attacks by evaluating how introducing manipulated or mislabeled data into potential training or fine-tuning datasets could compromise the model. They assess the impact on model accuracy, fairness, or the potential creation of exploitable backdoors, informing defenses around data validation and provenance.

Learn more from the following resources:

• @article@AI Poisoning
• @paper@Detecting and Preventing Data Poisoning Attacks on AI Models
• @paper@Poisoning Web-Scale Training Data (arXiv)