Mitigating the OWASP Top 10 For Large Language Models Applications using Intelligent Agents

TL;DR

This paper introduces a framework using LLM-enabled intelligent agents to proactively mitigate the OWASP Top 10 security vulnerabilities in large language model applications, enhancing their security and resilience.

Contribution

It proposes a novel framework leveraging intelligent agents to identify and counteract security threats in LLM applications, addressing a critical security gap.

Findings

Framework effectively identifies security threats in real-time

Enhances protection against OWASP Top 10 vulnerabilities

Provides a blueprint for future security improvements

Abstract

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.