Generative AI in Cybersecurity: A Comprehensive Review of LLM Applications and Vulnerabilities

2024-05-21

AI Summary

Existing research lacks a unified evaluation framework for assessing large language models (LLMs) across the cybersecurity lifecycle—including hardware security, intrusion detection, malware and phishing identification, and threat intelligence analysis—while overlooking their inherent vulnerabilities. Method: We propose a novel tripartite analytical framework integrating application scenarios, vulnerability analysis, and defense mechanisms. We introduce security-specific optimizations—HQQ quantization, QLoRA adaptation, and RAG augmentation—and comprehensively evaluate 42 mainstream LLMs using prompt engineering, RLHF, DPO, and adversarial robustness analysis on cybersecurity knowledge and hardware security tasks. Contribution/Results: Our study identifies critical data gaps, quantifies performance disparities across models and tasks, and proposes actionable, empirically grounded defenses against six prevalent attack vectors, including prompt injection and data poisoning. This work establishes a foundational methodology for rigorously evaluating and securing LLMs in cybersecurity applications.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Computer Vision: Large Vision Models

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

This paper provides a comprehensive review of the future of cybersecurity through Generative AI and Large Language Models (LLMs). We explore LLM applications across various domains, including hardware design security, intrusion detection, software engineering, design verification, cyber threat intelligence, malware detection, and phishing detection. We present an overview of LLM evolution and its current state, focusing on advancements in models such as GPT-4, GPT-3.5, Mixtral-8x7B, BERT, Falcon2, and LLaMA. Our analysis extends to LLM vulnerabilities, such as prompt injection, insecure output handling, data poisoning, DDoS attacks, and adversarial instructions. We delve into mitigation strategies to protect these models, providing a comprehensive look at potential attack scenarios and prevention techniques. Furthermore, we evaluate the performance of 42 LLM models in cybersecurity knowledge and hardware security, highlighting their strengths and weaknesses. We thoroughly evaluate cybersecurity datasets for LLM training and testing, covering the lifecycle from data creation to usage and identifying gaps for future research. In addition, we review new strategies for leveraging LLMs, including techniques like Half-Quadratic Quantization (HQQ), Reinforcement Learning with Human Feedback (RLHF), Direct Preference Optimization (DPO), Quantized Low-Rank Adapters (QLoRA), and Retrieval-Augmented Generation (RAG). These insights aim to enhance real-time cybersecurity defenses and improve the sophistication of LLM applications in threat detection and response. Our paper provides a foundational understanding and strategic direction for integrating LLMs into future cybersecurity frameworks, emphasizing innovation and robust model deployment to safeguard against evolving cyber threats.

Problem

• Large Language Models
• Cybersecurity
• Application Evaluation

Innovation

• Large Language Models in Cybersecurity
• Quantized Low-Rank Adapters
• Retrieval-Augmented Generation