Vectors Are Not Neutral: Sensitive-Information Inference from Exported LLM Representations in Summarization
arXiv:2605.26433v1 Announce Type: new Abstract: 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 sensi
The proliferation of advanced LLM summarization systems and the increasing use of vector databases for AI applications make this an immediate concern for data privacy and security.
This research highlights a critical vulnerability in AI systems, where seemingly innocuous data representations can leak sensitive information, compelling a re-evaluation of data handling and access controls in AI deployments.
The understanding of what constitutes 'private' or 'sensitive' data within AI systems expands to include derived vector representations, necessitating new security paradigms for AI development and deployment.
- · AI security researchers
- · Data privacy compliance solutions
- · Secure AI development platforms
- · Organizations with lax AI data governance
- · Healthcare providers using commercial LLMs without robust safeguards
- · Developers relying solely on source document access restrictions
Increased scrutiny on data-vectorization processes and access controls within AI systems.
Development of new privacy-preserving techniques specifically for AI-generated vector embeddings.
Potential for new regulatory frameworks explicitly addressing 'inferred' sensitive data from AI residuals.
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Read at arXiv cs.CL