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Extraction attack risk for AI

Extraction attack risk for AI

Risks associated with input


An attack that attempts to copy or steal the AI model by appropriately sampling the input space, observing outputs, and building a surrogate model, is known as an extraction attack.

Why is extraction attack a concern for foundation models?

A successful attack mimics the model, enabling the attacker to repurpose it for their benefit such as eliminating a competitive advantage or causing reputational harm.

Parent topic: AI risk atlas

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