Large language models (LLMs) are revolutionizing how we interact with information, powering sophisticated chatbots and virtual assistants. But a concerning vulnerability lurks within a common LLM architecture known as Retrieval-Augmented Generation (RAG). RAG systems enhance LLM responses by retrieving relevant information from external knowledge bases. However, this very mechanism can be exploited to leak sensitive data from those private knowledge bases. Imagine a pirate cleverly coaxing a parrot (the LLM) into revealing the location of buried treasure (the knowledge base). This is the essence of a new, automated attack method that adaptively probes RAG systems. The “pirate” algorithm uses an open-source LLM and readily available tools. It employs a system of “anchors”—keywords related to the target knowledge base—to craft increasingly effective queries. Each query is like a carefully worded question designed to trick the LLM into revealing more of the hidden knowledge. A relevance-based system refines the anchors, discarding less effective ones and prioritizing those that trigger the release of new information. Tests on various RAG systems simulating medical, educational, and research assistants demonstrated that this method outperforms existing attack techniques in both the amount and diversity of leaked data. In some cases, the pirate algorithm extracted a substantial portion of the private knowledge base, highlighting the vulnerability of current RAG systems. This research underscores the urgent need for more robust security measures. While techniques like adjusting the number of retrieved chunks and modifying prompt structures can help, they are not foolproof. The advent of "Guardian" LLMs—designed to filter unsafe content—offers a potential defense. However, early tests show that these guardians are not yet sophisticated enough to consistently block these data leaks while also allowing legitimate queries. The race is on to develop stronger safeguards for LLMs as they become increasingly integrated into our lives. The potential for misuse is real, and protecting private data in the age of intelligent assistants is paramount.