PoisonedRAG: Safeguarding LLMs Against Knowledge Corruption
Analysis
This article delves into the fascinating world of securing Large Language Models (LLMs) by exploring 'PoisonedRAG,' a technique that reveals vulnerabilities in Retrieval-Augmented Generation (RAG) systems. It provides an insightful look into how attackers can manipulate the knowledge base, offering valuable insights into defense strategies.
Key Takeaways
- •PoisonedRAG focuses on data poisoning, a form of attack where malicious content is injected into the knowledge base.
- •The article explores how attackers can manipulate RAG outputs by subtly altering the information retrieved.
- •It aims to provide a practical understanding, including a Python-based demo, to showcase the impact of such attacks.
Reference / Citation
View Original"The core is to attack RAG by polluting the knowledge base, which is to mix a little 'poison document' into the knowledge base (KB) to twist the output of RAG for specific questions to the content the attacker aimed for."
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Zenn LLMJan 30, 2026 01:00
* Cited for critical analysis under Article 32.