Inadvertent Context Leakage in Language Models
Researchers demonstrate that sensitive data merely present in an LLM's context window can leak through normal interaction—raising urgent questions for enterprises feeding credentials, PII, or financial records into AI agents.
Summary written by editorial AI · Source link below
arXiv:2608.19857v2 Announce Type: replace-cross Abstract: For AI agents to be useful beyond simple chat, they must hold sensitive user context such as calendars, credentials, health records, and financial data. We study whether the mere presence of such secrets in a model's context window introduces hidden correlations into the model's benign outputs, allowing reconstruction even when the model correctly refuses direct extraction. We further study whether an adversary can actively engineer prom
Editorial Analysis
European enterprises increasingly feed sensitive data into LLM-based tools; inadvertent context leakage could trigger GDPR violations and expose credentials or financial information without any overt attack.
Conduct context-leakage assessments for all LLM deployments handling sensitive data and implement secret-redaction pipelines.
Research confirms AI language models can inadvertently expose sensitive enterprise data simply held in their working memory, with regulatory and operational implications.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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