Data poisoning

Data poisoning refers to manipulating training data used to train an LLM model. This manipulation can be malicious, with attackers intentionally injecting false, misleading, or unintentional data points, where errors or biases in the original data set are included. In either case, data poisoning can lead to a tainted model that learns incorrect patterns, produces biased predictions, and becomes untrustworthy.

117 controls mitigate this threat across 15 domains