大语言模型安全的挑战与机遇

付志远; 陈思宇; 陈骏帆; 海翔; 石岩松; 李晓琦; 李益红; 岳秋玲; 张玉清

引用本文：

付志远,陈思宇,陈骏帆,海翔,石岩松,李晓琦,李益红,岳秋玲,张玉清.大语言模型安全的挑战与机遇[J].信息安全学报,2024,9(5):26-55 [点击复制]
FU Zhiyuan,CHEN Siyu,CHEN Junfan,HAI Xiang,SHI Yansong,LI Xiaoqi,LI Yihong,YUE Qiuling,ZHANG Yuqing.Challenges and Opportunities of Large Language Model Security[J].Journal of Cyber Security,2024,9(5):26-55 [点击复制]

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大语言模型安全的挑战与机遇
付志远^1,2, 陈思宇^1,2, 陈骏帆^1,2, 海翔^1,2, 石岩松^1,2, 李晓琦¹, 李益红¹, 岳秋玲¹, 张玉清^1,2
0 字体:加大+\|默认\|缩小-
(1.海南大学网络空间安全学院海口中国 570228;2.国家计算机网络入侵防范中心中国科学院大学北京中国 101408)

摘要:

大语言模型的技术进步不仅推动了人工智能领域的快速发展,也带来了前所未有的安全挑战。大语言模型在处理自然语言理解和生成等任务时的高效,使其在多个行业中得到广泛应用,包括自动化客服、内容创作、情感分析、医疗诊断、金融分析以及法律咨询等。然而,随着应用的深入,大语言模型面临的安全威胁也日益显现,如模型被恶意利用生成虚假信息、隐私泄露问题以及模型的偏见和不公平等问题。本文深入探讨了大语言模型的安全挑战,并分析了如何利用这些模型来增强传统安全方法。我们首先综合分析了近年来在国际学术会议和期刊上发表的本领域论文,并进行了详尽的归纳和总结。接着,我们从数据和隐私保护、法律与伦理、攻击及其防御三个角度详细分析了大语言模型自身面临的安全问题以及现有的解决方案。同时,我们还总结了一系列大语言模型在传统安全领域中的应用案例,包括网络安全、物理安全和信息安全。进一步,我们调研和归纳了国内外企业在大语言模型领域的最新尝试,许多企业正在积极探索如何将大语言模型赋能于实际安全业务中。最后,我们探讨了面临的挑战与机遇,并提出了解决这些问题的可行策略和建议。通过本文的深入分析,我们希望能够提高公众和业界对大语言模型安全问题的关注,并为未来的研究和应用提供方向和启发,推动整个行业朝着更加安全和可靠的方向发展。

关键词: 大语言模型人工智能安全隐私安全防御措施

DOI：10.19363/J.cnki.cn10-1380/tn.2024.09.02

投稿时间：2024-03-31修订日期：2024-05-31

基金项目:本课题得到国家重点研发计划项目(No.2023YFB3106400,No.2023QY1202)、国家自然科学基金重点项目(No.U2336203,No.U1836210)、海南省重点研发计划项目(No.GHYF2022010)、北京市自然科学基金(No.4242031)、海南省教育厅项目(No.HNJG2023-10)、海南省省属科研院所技术创新项目(No.KYYSGY2023-003,No.SQKY2022-0039)。

Challenges and Opportunities of Large Language Model Security

FU Zhiyuan^1,2, CHEN Siyu^1,2, CHEN Junfan^1,2, HAI Xiang^1,2, SHI Yansong^1,2, LI Xiaoqi¹, LI Yihong¹, YUE Qiuling¹, ZHANG Yuqing^1,2

(1.College of Cyberspace Security, Hainan University, Haikou 570228, China;2.National Computer Network Intrusion Protection Center, University of Chinese Academy of Sciences, Beijing 101408, China)

Abstract:

The technological advancements of large language models have not only accelerated the rapid development of the field of artificial intelligence but also brought unprecedented security challenges. The efficiency of large language models in handling tasks such as natural language understanding and generation has led to their widespread application in various industries, including automated customer service, content creation, sentiment analysis, medical diagnosis, financial analysis, and legal consultation. However, with the deepening of these applications, the security threats faced by large language models have become increasingly apparent, such as malicious use to generate false information, privacy leakage issues, and problems of bias and unfairness in the models. This paper explores the security challenges of large language models in depth and analyzes how these models can be used to enhance traditional security methods. First, we comprehensively analyze papers in this field published at international academic conferences and journals in recent years, providing a detailed summary and synthesis. Then, we analyze the security issues faced by large language models and existing solutions from three perspectives: data and privacy protection, law and ethics, and attacks and defenses. We also summarize a series of application cases of large language models in traditional security fields, including cybersecurity, physical security, and information security. Furthermore, we investigate and summarize the latest attempts by domestic and international enterprises in the field of large language models, where many companies are actively exploring how to empower actual security businesses with large language models. Finally, we discuss the challenges and opportunities faced and propose feasible strategies and recom-mendations to address these issues. Through this in-depth analysis, we hope to raise public and industry awareness of the security issues of large language models and provide directions and insights for future research and applications, promoting the entire industry towards a safer and more reliable direction.

Key words: large language models AI security privacy & security defensive techniques