用智能代理实时防御大模型应用的十大安全风险
Mitigating the OWASP Top 10 For Large Language Models Applications using Intelligent Agents
- 利用大模型驱动的智能代理主动识别和应对威胁
- 为大模型应用提供实时安全防护框架，提升系统韧性
- 适合关注AI安全的开发者与安全研究人员
大型语言模型（LLMs）已成为自然语言处理、机器翻译等领域的变革性技术，但其广泛应用也引发了诸多安全问题。开源网络应用安全项目（OWASP）列出了大模型应用中的十大安全漏洞。本文提出一种基于大模型智能代理的安全防护框架，能够实时识别、评估并应对这些安全威胁。该框架为未来研究提供了初步蓝图，旨在增强大模型的安全性，抵御快速演进环境中的新兴风险。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have emerged as a transformative and disruptive technology, enabling a wide range of applications in natural language processing, machine translation, and beyond. However, this widespread integration of LLMs also raised several security concerns highlighted by the Open Web Application Security Project (OWASP), which has identified the top 10 security vulnerabilities inherent in LLM applications. Addressing these vulnerabilities is crucial, given the increasing reliance on LLMs and the potential threats to data integrity, confidentiality, and service availability. This paper presents a framework designed to mitigate the security risks outlined in the OWASP Top 10. Our proposed model leverages LLM-enabled intelligent agents, offering a new approach to proactively identify, assess, and counteract security threats in real-time. The proposed framework serves as an initial blueprint for future research and development, aiming to enhance the security measures of LLMs and protect against emerging threats in this rapidly evolving landscape.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。