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Membership inference attack risk for AI

Membership inference attack risk for AI

Risks associated with input
Traditional AI risk


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

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