Prompt Stealing Attacks Against Large Language Models

TL;DR

This paper introduces prompt stealing attacks against large language models, demonstrating how well-designed prompts can be extracted from generated answers, highlighting new security concerns in prompt engineering.

Contribution

The paper proposes a novel prompt stealing attack framework with modules for parameter extraction and prompt reconstruction, revealing vulnerabilities in LLM prompt security.

Findings

Effective prompt stealing demonstrated on LLMs

High accuracy in identifying prompt types and reconstructing prompts

Highlights security risks in prompt engineering practices

Abstract

The increasing reliance on large language models (LLMs) such as ChatGPT in various fields emphasizes the importance of ``prompt engineering,'' a technology to improve the quality of model outputs. With companies investing significantly in expert prompt engineers and educational resources rising to meet market demand, designing high-quality prompts has become an intriguing challenge. In this paper, we propose a novel attack against LLMs, named prompt stealing attacks. Our proposed prompt stealing attack aims to steal these well-designed prompts based on the generated answers. The prompt stealing attack contains two primary modules: the parameter extractor and the prompt reconstruction. The goal of the parameter extractor is to figure out the properties of the original prompts. We first observe that most prompts fall into one of three categories: direct prompt, role-based prompt, and in-context prompt. Our parameter extractor first tries to distinguish the type of prompts based on the generated answers. Then, it can further predict which role or how many contexts are used based on the types of prompts. Following the parameter extractor, the prompt reconstructor can be used to reconstruct the original prompts based on the generated answers and the extracted features. The final goal of the prompt reconstructor is to generate the reversed prompts, which are similar to the original prompts. Our experimental results show the remarkable performance of our proposed attacks. Our proposed attacks add a new dimension to the study of prompt engineering and call for more attention to the security issues on LLMs.