arXiv (All)
open access
Inadvertent Context Leakage in Language Models
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, allo
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- PrivDrift: Auditing User-Secret Leakage Under Topic Drift in Active LLM Conversations#1078165
- Inadvertent Context Leakage in Language Models#471189
- User-Side Contextual Phenomena in Long-Term Human-AI Interaction#1070973
- Contextual Contamination: A Descriptive Case Study of Drift in a Goal-Aware LLM Dialogue Amplified by Gender-Bias#204181
- What Does the Server See? Understanding Privacy Leakage from Large Language Models in Split Inference#222556
Record · ID 1534136
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive