Imagine asking a chatbot a simple question like, "Where were the 2024 Olympics held?" and getting the completely wrong answer. Now, imagine this happening not because the AI is hallucinating, but because a hacker has subtly poisoned its knowledge base. This is the alarming reality revealed by new research on 'retrieval prompt hijacking' attacks. Retrieval-augmented generation (RAG) systems, which power many of today’s advanced chatbots, combine the conversational abilities of large language models (LLMs) with access to external databases. This makes them more factual and adaptable than LLMs alone. However, this reliance on external data creates a new attack surface for hackers. Researchers have demonstrated a novel vulnerability called HIJACKRAG, where malicious actors inject carefully crafted text snippets into the chatbot's knowledge base. These snippets are designed to be highly relevant to specific user questions, so when those questions are asked, the malicious text gets retrieved and used to generate the answer. The result? The chatbot unknowingly becomes a puppet, parroting the hacker's desired responses, potentially spreading misinformation, phishing for personal data, or even manipulating users into harmful actions. This attack isn't just theoretical. Experiments show it's highly effective against popular chatbot architectures, with success rates reaching over 90%. Even worse, defenses like paraphrasing user queries or increasing the amount of retrieved information offer little protection. This research highlights a critical security gap in the rapidly evolving field of AI. As RAG systems become more prevalent, safeguarding them against these types of attacks will be crucial to ensure trust and prevent misuse. The research team suggests that new, more robust defense strategies are urgently needed to protect these systems and maintain the integrity of information in the age of AI.