Large language models (LLMs) have revolutionized how we interact with AI, allowing us to instruct them with simple prompts. But this convenience comes with a hidden cost: a significant privacy risk. New research reveals that the data used in these prompts can be easily leaked, potentially exposing sensitive information. Think of it like this: you give an LLM a few examples of patient diagnoses to teach it to identify illnesses. The study found these very examples are vulnerable to attacks that can reconstruct them from the LLM's responses. This vulnerability stems from the LLM's higher confidence in predicting data it has already seen in the prompt. This makes it easier for an attacker, with just access to the LLM's output probabilities, to infer whether a specific data point was part of the original prompt. Surprisingly, the research also shows that this risk is even higher with in-context learning (prompting) compared to traditional fine-tuning methods. The good news? Researchers have found a potential solution in prompt ensembling. By combining predictions from multiple prompted models trained on disjoint datasets, they can effectively mask the telltale signs of prompt memorization and reduce the risk of data leakage. This technique essentially dilutes the influence of any single prompt, making it harder for attackers to reconstruct the original data. This study highlights a crucial challenge in the rapidly evolving landscape of AI. As LLMs become more integrated into our daily lives, ensuring the privacy of data used to instruct them is paramount. This research encourages further exploration of privacy-preserving techniques for LLMs, suggesting that larger models and clever ensembling strategies could be key to safeguarding sensitive information in the age of AI prompting.