Prompt Stealing Attacks Against Large Language Models sheds light on a new type of security concern for LLMs such as ChatGPT. The paper uncovers how attackers can steal expertly crafted prompts by analyzing the outputs of these models.

• The advent of ‘prompt engineering’ has raised the stakes in the quality of LLM outputs.
• The paper proposes a concept of ‘prompt stealing’ attack, where attackers reverse-engineer the prompts from model answers.
• A two-module attack process is described, consisting of a parameter extractor and a prompt reconstructor.
• The performance of the attack shows the need for more secure and robust systems against such vulnerabilities.
• Brings a fresh perspective to the discourse around LLM security and prompt engineering.

I believe this research underscores a novel aspect of AI security surrounding the proprietary aspects of prompt engineering. It calls for an industry-wide consideration of how to protect intellectual property in the realm of LLMs and might lead to advances in secure prompting techniques.

Discover more about the study here.