The critical challenge of prompt injection attacks in Large Language Models
(LLMs) integrated applications, a growing concern in the Artificial
Intelligence (AI) field. Such attacks, which manipulate LLMs through natural
language inputs, pose a significant threat to the security of these
applications. Traditional defense strategies, including output and input
filtering, as well as delimiter use, have proven inadequate. This paper
introduces the 'Signed-Prompt' method as a novel solution. The study involves
signing sensitive instructions within command segments by authorized users,
enabling the LLM to discern trusted instruction sources. The paper presents a
comprehensive analysis of prompt injection attack patterns, followed by a
detailed explanation of the Signed-Prompt concept, including its basic
architecture and implementation through both prompt engineering and fine-tuning
of LLMs. Experiments demonstrate the effectiveness of the Signed-Prompt method,
showing substantial resistance to various types of prompt injection attacks,
thus validating its potential as a robust defense strategy in AI security.

大语言模型中快速注入攻击的关键挑战以及引起人工智能领域日益关注。传统的防御策略不够有效，本文提出了一种新颖的解决方案 ——“签名提示”。该方法通过将敏感指令签名并由授权用户使用，使得语言模型可以辨别可信指令来源。本文详细介绍了快速注入攻击模式的分析，并通过提示工程和语言模型的微调来实现 “签名提示” 概念的基本框架和实现。实验证明了 “签名提示” 方法的有效性，对各种类型的快速注入攻击具有相当的抵抗能力，从而验证了其作为一种强大的人工智能安全防御策略的潜力。