RECOVER PROMPT USING REVERSE ENGINEERING

Large Language Models (LLMs) have significantly advanced natural language processing tasks but are heavily reliant on well-crafted prompts for optimal performance.However, manual prompt engineering is timeconsuming, often sub-optimal, and lacks robustness to various perturbations in input prompts.Additionally, there is a growing need to address the security implications of prompt extraction attacks.Existing research has proposed automated methods for prompt engineering, ranging from rewriting under-optimized prompts to generating highquality human-like prompts from scratch.Despite these advancements, challenges persist in achieving prompt effectiveness, robustness, and security.