Vectors Are Not Neutral: Sensitive-Information Inference from Exported LLM Representations in Summarization
Large language model (LLM) summarization systems may pass compact vector representations of private inputs to downstream retrieval, monitoring, audit, or analytic workflows. Even when source documents remain access-restricted, derived vectors may be handled under different access controls and still support sensitive-information inference, creating a residual information-disclosure risk. We study this issue in clinical discharge-summary generation as a high-stakes case study, using electronic health record (EHR)-recorded race as a controlled sensitive-label audit. We audit two artifacts that a system might retain or expose to downstream components: the final prompt-token hidden state and t...
| Authors | Weixin Liu, Bowen Qu, Juming Xiong et al. |
|---|---|
| Primary category | cs.CL |
| Published | 26 May 2026 |
| Last updated at source | 26 May 2026 |
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