探索大型语言模型中的漏洞与保护：一项综述
Exploring Vulnerabilities and Protections in Large Language Models: A Survey
AI总结：
本综述系统考察了大型语言模型在提示词攻击和对抗性攻击两大领域的安全漏洞，探讨了各类威胁的原理与影响，并提出了相应的防御策略与稳健框架，为构建能抵御复杂攻击的弹性AI系统提供了重要参考。
摘要：
随着大型语言模型（LLMs）日益成为各种AI应用的关键组件，理解其安全漏洞及防御机制的有效性至关重要。本综述考察了LLMs的安全挑战，重点关注两个主要领域：提示词攻击和对抗性攻击，每种攻击都有特定的威胁类型。在提示词攻击下，我们探讨了提示词注入和越狱攻击，讨论了它们的工作原理、潜在影响以及缓解方法。类似地，我们分析了对抗性攻击，将其分解为数据投毒攻击和后门攻击。这种结构化的考察有助于我们理解这些漏洞与可实施的防御策略之间的关系。综述强调了这些安全挑战，并讨论了保护LLMs免受这些威胁的稳健防御框架。通过详细说明这些安全问题，本综述为创建能够抵御复杂攻击的弹性AI系统这一更广泛的讨论做出了贡献。
英文摘要：
As Large Language Models (LLMs) increasingly become key components in various AI applications, understanding their security vulnerabilities and the effectiveness of defense mechanisms is crucial. This survey examines the security challenges of LLMs, focusing on two main areas: Prompt Hacking and Adversarial Attacks, each with specific types of threats. Under Prompt Hacking, we explore Prompt Injection and Jailbreaking Attacks, discussing how they work, their potential impacts, and ways to mitigate them. Similarly, we analyze Adversarial Attacks, breaking them down into Data Poisoning Attacks and Backdoor Attacks. This structured examination helps us understand the relationships between these vulnerabilities and the defense strategies that can be implemented. The survey highlights these security challenges and discusses robust defensive frameworks to protect LLMs against these threats. By detailing these security issues, the survey contributes to the broader discussion on creating resilient AI systems that can resist sophisticated attacks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
