LATIDIA · Ciberseguridad

Filtración de datos privados desde un estado recurrente en la inferencia de LLM

arXiv:2611.16048v1 Tipo de anuncio: nuevo Resumen: Los servicios de LLM personalizados introducen datos privados e instrucciones de aplicación en un «prompt» y, a continuación, conservan representaciones de ese contexto para realizar inferencias posteriores. LLM de estado latente

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arXiv:2611.16048v1 Announce Type: new Abstract: Personalized LLM services place private facts and application instructions into a prompt, then retain representations of that context for later inference. Latent-state LLMs compress part of this history into fixed-size recurrent state. The information preserved by its learned updates is implicit, and later computation can expose it even when an observer receives neither the private text nor the responses. We investigate privacy leakage from recurrent state through partial-state probing and physical observation of its use. Our methodology separates recoverable information, persistence through public dialogue, causal influence on later decisions, and leakage through power. Experiments with released hybrid implementations show that a small state observation supports