Large language models (LLMs) are getting smarter and can now handle much longer chunks of text. This allows them to answer complex questions by searching through huge amounts of information. But there's a catch: loading all this data into an LLM's memory makes it a potential goldmine for hackers. New research explores how attackers could exploit these long-context LLMs (LCLMs) to figure out if specific, potentially sensitive documents are included in the LCLM's database. Imagine a medical LCLM containing patient records—a successful attack could reveal someone's private health information. Researchers developed six different attack strategies, some requiring access to the model's inner workings, others only needing the final text output. The most effective methods used clever prompts to trick the LCLM into revealing information through the text it generated or the 'confidence' it had in its predictions. The alarming finding: these attacks worked surprisingly well. This highlights a serious privacy risk in LCLMs, emphasizing the need for better safeguards. As AI systems handle increasingly sensitive data, protecting it from these kinds of attacks becomes paramount. The next step is developing strong defenses to keep our private information safe in the age of powerful AI.