Large language models (LLMs) are revolutionizing how we interact with technology, offering human-like text comprehension and generation. However, this power comes with a vulnerability: prompt injection attacks. These attacks involve crafting malicious inputs that hijack the LLM's intended behavior, potentially leading to the divulging of sensitive information or the generation of harmful content. Think of it like a skilled hacker subtly manipulating a conversation to trick someone into revealing their password. Current defenses against these attacks often fall short, failing to detect more sophisticated injection attempts. This is where Palisade comes in. Researchers have developed Palisade, a multi-layered security framework designed to fortify LLMs against these attacks. Palisade acts as a gatekeeper, screening incoming prompts before they reach the LLM. It employs three distinct layers of defense: a rule-based filter for catching obvious malicious inputs, a BERT-based machine learning classifier trained to identify subtle injection patterns, and a companion LLM acting as a final line of defense. This layered approach ensures that even if one layer fails, others are there to catch the threat. Testing shows that Palisade significantly reduces the chance of a malicious prompt slipping through, bolstering the security and reliability of LLMs. While no system is foolproof, Palisade represents a significant step forward in protecting these powerful language models from manipulation. Future research will focus on refining these layers and exploring new defense mechanisms to keep LLMs one step ahead of malicious actors. As LLMs become increasingly integrated into our lives, robust security frameworks like Palisade are crucial for ensuring their safe and ethical use.