Weaponizing Language Models for Cybersecurity Offensive Operations: Automating Vulnerability Assessment Report Validation; A Review Paper

TL;DR

This paper explores how Large Language Models can automate and enhance the validation of Vulnerability Assessment reports, aiming to reduce false positives and improve cybersecurity efficiency.

Contribution

It introduces a novel approach for using LLMs in automating VA report validation, filling a gap in offensive cybersecurity applications.

Findings

LLMs can automate VA report validation effectively.

The approach reduces false positives in vulnerability reports.

Automation improves validation accuracy and efficiency.

Abstract

This, with the ever-increasing sophistication of cyberwar, calls for novel solutions. In this regard, Large Language Models (LLMs) have emerged as a highly promising tool for defensive and offensive cybersecurity-related strategies. While existing literature has focused much on the defensive use of LLMs, when it comes to their offensive utilization, very little has been reported-namely, concerning Vulnerability Assessment (VA) report validation. Consequentially, this paper tries to fill that gap by investigating the capabilities of LLMs in automating and improving the validation process of the report of the VA. From the critical review of the related literature, this paper hereby proposes a new approach to using the LLMs in the automation of the analysis and within the validation process of the report of the VA that could potentially reduce the number of false positives and generally enhance efficiency. These results are promising for LLM automatization for improving validation on reports coming from VA in order to improve accuracy while reducing human effort and security postures. The contribution of this paper provides further evidence about the offensive and defensive LLM capabilities and therefor helps in devising more appropriate cybersecurity strategies and tools accordingly.