arXiv (All)
open access
RAGLeak: Supply-Chain Privacy Backdoors in Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) enables large language models to access external knowledge during inference, but its reliance on external knowledge bases also introduces new attack surfaces. Existing data extraction attacks against RAG systems rely on either prompt injection, which heavily depe
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
- RAGLeak: Supply-Chain Privacy Backdoors in Retrieval-Augmented Generation#1381142
- Privacy-Preserving RAG by Concealing Sensitive Information from External LLMs#973310
- Mind the Hook: Source-Level Auditing of Privacy Defenses in Retrieval-Augmented Generation#441704
- A Security Meta-Model for Retrieval-Augmented Generation Systems#1249173
- A Security Meta-Model for Retrieval-Augmented Generation Systems#1242685
Record · ID 1401126
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