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

2024-02-26

International Conference Control and Robots

AI Summary

This work proposes an LLM-driven intelligent agent framework that addresses the susceptibility of large language model (LLM) applications to the OWASP Top 10 security vulnerabilities, which threaten data integrity, confidentiality, and service availability. By deeply integrating intelligent agent technology with LLMs, the framework enables real-time identification, risk assessment, and proactive response to these vulnerabilities. It establishes a scalable and automated active defense mechanism, offering both foundational architecture and a practical blueprint for developing highly trustworthy LLM-based systems.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Natural Language Processing: (Large) Language Models

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

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.

Problem

• Large Language Models
• OWASP Top 10
• Security vulnerabilities
• Intelligent Agents
• LLM applications

Innovation

• Intelligent Agents
• LLM Security
• OWASP Top 10
• Real-time Threat Mitigation
• Security Framework