arXiv (All)
open access
ToxicRAG: Compromising Retrieval-Augmented Generation Systems via Single-Shot Knowledge Poisoning Attacks
Retrieval-Augmented Generation (RAG) can ground large language model (LLM) outputs in external evidence, but it also exposes the system to knowledge poisoning. Representative attacks use multiple injected documents or templates that directly assert a target answer. We present ToxicRAG, a one-documen
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
- ToxicRAG: Compromising Retrieval-Augmented Generation Systems via Single-Shot Knowledge Poisoning Attacks#673418
- Socrates-RAG: Premise-Directed Inquiry against Coordinated Evidence Poisoning#1130615
- LENS: The Sum Is Worse Than the Parts for Set-Level Poisoning in Retrieval-Augmented Generation#1129752
- LENS: The Sum Is Worse Than the Parts for Set-Level Poisoning in Retrieval-Augmented Generation#1108620
- One Shot Dominance: Knowledge Poisoning Attack on Retrieval-Augmented Generation Systems#128388
Record · ID 998642
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