Researchers can now reverse-engineer LLM prompts from output text with near-perfect accuracy

Researchers at IIT Bombay and Adobe Research have developed an inverse language model that reconstructs the original prompt from an LLM's output with near-perfect accuracy. The method, called 'Previous-Token Prediction,' does not require access to model weights and works across different models. This poses a serious security risk for companies relying on proprietary system prompts.