Large language models play a crucial role in modern natural language processing technologies. However, their extensive use also introduces potential security risks, such as the possibility of black-box attacks. These attacks can embed hidden malicious features into the model, leading to adverse consequences during its deployment. This paper investigates methods for black-box attacks on large language models with a three-tiered defense mechanism. It analyzes the challenges and significance of these attacks, highlighting their potential implications for language processing system security. Existing attack and defense methods are examined, evaluating their effectiveness and applicability across various scenarios. Special attention is given to the detection algorithm for black-box attacks, identifying hazardous vulnerabilities in language models and retrieving sensitive information. This research presents a methodology for vulnerability detection and the development of defensive strategies against black-box attacks on large language models.

翻译：大型语言模型在现代自然语言处理技术中扮演着关键角色。然而，其广泛应用也带来了潜在的安全风险，例如可能存在的黑盒攻击。这类攻击能够将隐藏的恶意特征嵌入模型，导致其在部署过程中产生不利后果。本文研究了针对具有三层防御机制的大型语言模型的黑盒攻击方法，分析了此类攻击面临的挑战与重要意义，并阐明了其对语言处理系统安全的潜在影响。本文系统考察了现有的攻击与防御方法，评估了它们在不同场景下的有效性与适用性。研究特别关注黑盒攻击的检测算法，旨在识别语言模型中的危险漏洞并检索敏感信息。本研究提出了一种针对大型语言模型黑盒攻击的漏洞检测方法学，并制定了相应的防御策略。