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Security boundary

Weights Disclosure

Weights Disclosure refers to revealing the internal parameters (or “weights”) of a trained generative AI model. The weights are the mathematical values learned during training that enable the model to generate its outputs. These weights are typically proprietary intellectual property, representing the distilled knowledge obtained from potentially vast and valuable training data.

If an attacker gains access to the model’s weights, they can replicate the model’s capabilities without paying for training costs, analyze its vulnerabilities, or reverse-engineer the training data distribution. Weights disclosure can undermine a company’s competitive advantage, expose sensitive training secrets, and enable the creation of “knock-off” models.

Example:

Imagine a cutting-edge image generator model trained by a startup at great expense. An attacker finds a bug in the model hosting platform that allows them to download the model’s weights. Now the attacker can run this high-quality image generator on their own hardware without licensing fees, replicate its functionalities, or even modify it to remove safeguards. The startup loses its competitive edge, and its investment in training the model is compromised by the weights disclosure.