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Token generation timing can leak critical architectural and optimization details from language models, exposing vulnerabilities in their deployment.
Safety classifiers leak surprisingly sensitive information: a boundary-targeted attack recovers 19% of user distress conversations from the training data, far exceeding existing membership inference methods.
