RAG Pipelines Face 'Knowledge Injection' Attacks That Hijack LLM Responses

Retrieval-Augmented Generation (RAG), widely used to ground large language models in private or real-time data, contains a critical vulnerability in its core architecture. Attackers can plant malicious instructions inside documents, web pages, or tool outputs that the RAG pipeline retrieves, causing the LLM to treat adversarial content as authoritative. This technique, called knowledge injection or indirect prompt injection, differs from direct prompt injection because the attacker targets the data source rather than the user's query. A detailed engineering analysis published on DEV Community outlines why standard RAG implementations are inherently fragile due to implicit trust in retrieved content. The article proposes a layered defense strategy covering source isolation, semantic threat detection, and dual-model orchestration to harden RAG systems against such attacks.