Large language models (LLMs) are revolutionizing how we interact with technology, but they also present new security risks. One such risk is prompt injection, a sneaky attack where malicious prompts trick LLMs into generating harmful or inappropriate content. Think of it like SQL injection, but for AI. Researchers are exploring ways to defend against these attacks, and a recent paper suggests a clever solution: embedding-based classifiers. These classifiers analyze the underlying structure of prompts, converting them into numerical representations called embeddings. By training machine learning models on these embeddings, researchers found they could effectively distinguish between malicious and benign prompts. Different embedding models and machine learning classifiers were tested, with Random Forest showing the most promise when paired with OpenAI’s embedding model. This method even outperformed existing state-of-the-art prompt injection detectors. While the research didn’t find a perfect way to visually separate good and bad prompts, the success of the classifiers suggests that this approach is a valuable step toward securing LLMs. Future research could explore neural network-based classifiers and expand this technique to other LLM vulnerabilities like indirect prompt injection, toxic content generation, and hallucinations, ultimately paving the way for safer and more reliable AI systems.