Imagine asking an AI assistant a simple question, like "What are the early signs of Alzheimer's?" and it cheerfully directs you to a malicious website. That's the unsettling reality explored in new cybersecurity research, revealing how Retrieval Augmented Generation (RAG), a popular technique to make AI smarter, can be exploited for malicious purposes. RAG works by connecting Large Language Models (LLMs) to external databases to provide up-to-date information. This process, however, creates a backdoor for attackers to inject bad links, promote scams, or even cause denial-of-service errors. Researchers tested this vulnerability on leading LLMs like Llama-3, Vicuna, and Mistral, and the results were alarming. By subtly poisoning the information retrieved by the LLM, they successfully injected malicious links with a startling success rate. The most effective attack involved "backdooring" the retrieval system itself, essentially training the AI to fetch malicious data on specific topics. While corpus poisoning, or injecting bad data into the database, proved simpler, it was also less effective. These findings expose a critical security gap in the rapidly evolving world of AI. As LLMs become more integrated into our daily lives, securing these systems from malicious attacks becomes paramount. This research is a wake-up call, urging developers to prioritize security and build more robust defenses to prevent AI from becoming a tool for harm.