Membership inference attack risk for AI
Given a trained model and a data sample, an attacker appropriately samples the input space, observing outputs to deduce whether that sample was part of the model's training. This is known as a membership inference attack.
Why is membership inference attack a concern for foundation models?
Identifying whether a data sample was used for training data can reveal what data was used to train a model, possibly giving competitors insight into how a model was trained and the opportunity to replicate the model or tamper with it.
Parent topic: AI risk atlas