Large language models (LLMs) are like talented actors who need a script—a system prompt—to guide their performance. These prompts often contain sensitive information, like the secret sauce of an AI-powered app, making them valuable targets for hackers. Imagine someone figuring out the hidden instructions behind your favorite AI tool and replicating it for free! That's the problem researchers are tackling with a new defense mechanism called PromptKeeper. LLMs, despite their impressive abilities, are susceptible to leaking these prompts, either through cleverly crafted adversarial queries or even through seemingly innocent regular user questions. Think of it like a magician inadvertently revealing their tricks. Existing defenses, like filtering user queries or adding warnings to the prompts themselves, have proven inadequate. PromptKeeper takes a different approach. It acts like a vigilant bodyguard, constantly monitoring the LLM's output for any hint of a leak. Instead of simply blocking potentially leaky responses, which could create vulnerabilities of its own, PromptKeeper cleverly regenerates the output without using the secret prompt. This keeps the LLM's performance intact while ensuring the 'secret instructions' remain hidden. Tests on real-world and synthetic prompts show PromptKeeper's effectiveness in protecting against both adversarial and regular query attacks, offering a robust solution to this growing privacy concern. While this research focuses on system prompt protection, the broader question of user privacy remains a challenge, highlighting the ongoing evolution of AI security in this rapidly developing field. As LLMs become more integrated into our lives, safeguarding these 'secret instructions' is crucial for maintaining trust and preventing misuse.