Security and Privacy Challenges of Large Language Models: A Survey
Badhan Chandra Das Knight Foundation School of Computing and Information Sciences; Sustainability, Optimization, and Learning for InterDependent networks laboratory (solid lab), Florida International UniversityMiamiFloridaUnited States , M. Hadi Amini Knight Foundation School of Computing and Information Science, solid lab, Florida International UniversityMiamiFloridaUnited States and Yanzhao Wu Knight Foundation School of Computing and Information Sciences, Florida International UniversityMiamiFloridaUnited States Emails:
Abstract
Large language models (LLMs) have demonstrated extraordinary capabilities and contributed to multiple fields, such as generating and summarizing text, language translation, and question-answering. Nowadays, LLMs have become very popular tool in natural language processing (NLP) tasks, with the capability to analyze complicated linguistic patterns and provide relevant and appropriate responses depending on the context. While offering significant advantages, these models are also vulnerable to security and privacy attacks, such as jailbreaking attacks, data poisoning attacks, and personally identifiable information (PII) leakage attacks. This survey provides a thorough review of the security and privacy challenges of LLMs, along with the application-based risks in various domains, such as transportation, education, and healthcare. We assess the extent of LLM vulnerabilities, investigate emerging security and privacy attacks for LLMs, and review the potential defense mechanisms. Additionally, the survey outlines existing research gaps in this research area and highlights future research directions.
中文速览
大语言模型在带来强大生成、推理和交互能力的同时，也可能遭遇越狱、提示注入、后门、数据投毒、对抗攻击以及个人信息泄露等安全与隐私风险。研究通过系统梳理模型架构、攻击类型、典型案例和防御方法，进一步分析其在交通、教育、医疗等实际领域中的应用风险，并总结现有研究的不足与未来方向。结果表明，漏洞可能来自训练数据、模型开发者、部署系统和用户交互等多个环节，现有防御虽能缓解部分问题，却仍难以全面应对不断演化的攻击和隐私泄露。厘清这些风险及其防护思路，有助于研究者和企业更可靠地评估、部署大语言模型，尤其能为医疗、金融等对安全和隐私要求较高的场景提供依据。
原文 arXiv:2402.00888;中英对照 + 大白话阅读 https://aha.fim.ai/paper/2402.00888v2