arXiv (All)
open access
PrivDrift: Auditing User-Secret Leakage Under Topic Drift in Active LLM Conversations
Large language models increasingly operate as persistent assistants in user-facing, shared-session, and tool-augmented settings. When users disclose sensitive information during an active conversation, that information may remain behaviorally recoverable through later prompts even after the dialogue
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
- Demystifying the Privacy-Utility Trade-off in LLM Interactions#1069950
- Residual Vector-based Reconstruction as Long-Context Recall Regardless of Context Window Size#1070167
- Sleeping Secrets: How Fine-Tuning Reawakens Privacy Risks in Language Models#1160944
- Sleeping Secrets: How Fine-Tuning Reawakens Privacy Risks in Language Models#1156637
- Toward Robust Personalized Alignment for LLMs: Mitigating Persona Drift in Multi-Turn Dialogue#1000327
Record · ID 1078165
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