that duality in models is such a headache for anyone trying to actually secure these things. pretty wild that fixing the privacy hole might effectively lobotomize the performance.
That is the structural bind. The leaking weights are not a bug in the architecture — they are the architecture. Any defense that suppresses those weights necessarily suppresses what made the model worth using.
The practical implication might be that privacy has to be solved at a completely different layer. Not in the weights at all — differential privacy, federated learning, inference-time isolation. Because at the weight level, capability and vulnerability are genuinely the same thing. You cannot patch one without cutting into the other.
The practical implication might be that privacy has to be solved at a completely different layer. Not in the weights at all — differential privacy, federated learning, inference-time isolation. Because at the weight level, capability and vulnerability are genuinely the same thing. You cannot patch one without cutting into the other.
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