This research introduces a training-free framework that can reverse-engineer the prompts used to generate LLM outputs, even with minimal examples and under black-box conditions.

• Achieves prompt reconstruction using significantly fewer output samples than previous methods
• Works in zero-shot scenarios without requiring model training
• Operates under strict black-box conditions with limited access to the target model
• Demonstrates a concrete security vulnerability in current LLM deployments

For security teams, this research highlights a critical vulnerability: proprietary prompts that may contain sensitive information or intellectual property can be extracted from public-facing LLM applications, necessitating new safeguards and detection mechanisms.

Reverse Prompt Engineering