RAG frameworks vulnerable to prompt injection, even with advanced models

A security analysis of popular Retrieval-Augmented Generation (RAG) frameworks like LangChain, LlamaIndex, and Haystack revealed that all three are vulnerable to prompt injection attacks out-of-the-box. Even when using more advanced models such as GPT-5.1, the vulnerability persisted, with some attacks even worsening. The core issue identified is not the model's intelligence but the RAG architecture's tendency to treat retrieved text as trustworthy context, rather than untrusted data. Solutions involve treating retrieved text as data, delimiting it, and explicitly marking it as untrusted to the model, alongside other defenses like preventing unauthorized tool calls and keeping sensitive information out of the retriever's reach. AI

IMPACT Highlights critical security flaws in RAG architectures, emphasizing the need for better defenses beyond just using more advanced LLMs.

RANK_REASON The article details the security vulnerabilities of existing RAG frameworks and introduces a new tool for testing them.

Read on dev.to — LLM tag →

• DeepEval
• GPT-4o mini
• GPT-5.1
• haystack
• LangChain
• LlamaIndex
• Ragas
• rag-redteam

• product
• safety

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