Cryptographic Techniques for Concealing Neural Network Capabilities in AI Models

The paper demonstrates a method for concealing arbitrary neural network capabilities within AI models using cryptographic techniques, proving these hidden functions are resistant to standard elicitation methods. It introduces encrypted backdoor attacks that remain undetectable even with full model weight access, challenging current capability audit approaches.

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