The field of cybersecurity is witnessing a significant shift towards the integration of large language models (LLMs) to enhance threat detection and response. Recent developments have focused on improving the adaptability and reliability of LLMs in cybersecurity tasks, particularly in the face of emerging vulnerabilities and attack patterns. The use of Retrieval-Augmented Generation (RAG) has shown promise in strengthening LLMs for cybersecurity applications. Noteworthy papers in this area include: AthenaBench, which introduces a dynamic benchmark for evaluating LLMs in cyber threat intelligence, highlighting fundamental limitations in the reasoning capabilities of current LLMs. RAGDefender, a resource-efficient defense mechanism against knowledge corruption attacks in practical RAG deployments, demonstrating improved performance over existing state-of-the-art defenses.
Advancements in Large Language Models for Cybersecurity
Sources
Adapting Large Language Models to Emerging Cybersecurity using Retrieval Augmented Generation
AgentBnB: A Browser-Based Cybersecurity Tabletop Exercise with Large Language Model Support and Retrieval-Aligned Scaffolding
Repairing Responsive Layout Failures Using Retrieval Augmented Generation
AthenaBench: A Dynamic Benchmark for Evaluating LLMs in Cyber Threat Intelligence
Rescuing the Unpoisoned: Efficient Defense against Knowledge Corruption Attacks on RAG Systems
Scam Shield: Multi-Model Voting and Fine-Tuned LLMs Against Adversarial Attacks
Large Language Models for Cyber Security