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Security boundary

Layers Disclosure

Layers Disclosure involves revealing the structure or parameters of the neural network’s architecture beyond just the weights. While weights are crucial, the network’s layer configuration, size, and other architectural details also represent valuable trade secrets. Such information can provide insights into the techniques, optimizations, and proprietary innovations that make the model unique and effective.

If attackers know exactly how the layers are arranged, how they connect, and the model’s hyperparameters, they can more easily replicate its performance, find vulnerabilities, or tailor attacks. Layers disclosure weakens security by removing the obscurity of design and inviting reverse-engineering, model extraction, and targeted exploitation.

Example:

A company boasts a groundbreaking language model architecture with a novel attention mechanism. An attacker uses inference queries and analysis of response patterns to deduce that the model uses a specific number of Transformer blocks with custom attention layers. This knowledge is then cross-referenced with known architectures, allowing the attacker to reconstruct a close replica. As a result, the attacker gains competitive intelligence, while the company’s architectural advantage is diminished due to layers disclosure.