LATIDIA · Ciberseguridad

Póster: Un estudio preliminar de la inferencia de destilación de LLM

arXiv:2610.12137v1 Tipo de anuncio: nuevo Resumen: La destilación de modelos no autorizados, en la que un modelo se entrena en los resultados de un modelo de lenguaje grande patentado (LLM), es una amenaza creciente para los proveedores de modelos. Estudiamos di

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arXiv:2610.12137v1 Announce Type: new Abstract: Unauthorized model distillation, in which a model is trained on the outputs of a proprietary large language model (LLM), is a growing threat to model providers. We study distillation inference: determining whether a suspect model was distilled from another model or trained independently. We formulate this problem as a hypothesis test and estimate the behavior expected under each hypothesis by training shadow models: distilled shadow models learn from the teacher's reasoning traces, whereas independent shadow models learn only from reference answers. The auditor measures how closely each model predicts the teacher's reasoning outputs and then uses the shadow models to convert the suspect's score into