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.

翻译：大型语言模型（LLMs）已成为一项具有变革性和颠覆性的技术，在自然语言处理、机器翻译及其他领域实现了广泛应用。然而，LLMs的广泛集成也引发了诸多安全隐患，开放网络应用安全项目（OWASP）已明确指出LLM应用中固有的十大安全漏洞。鉴于对LLMs的依赖日益加深，以及这些漏洞对数据完整性、机密性和服务可用性构成的潜在威胁，解决这些安全问题至关重要。本文提出一个旨在缓解OWASP十大安全风险的框架。我们提出的模型利用基于LLM的智能代理，提供了一种实时主动识别、评估和应对安全威胁的新方法。该框架可作为未来研发的初步蓝图，旨在加强LLMs的安全防护措施，以应对这一快速发展领域中不断涌现的安全威胁。