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.

本研究针对现有文献对大型语言模型（LLMs）在网络安全攻势中的应用关注不足的问题，提出了一种新的方法来自动化和改进漏洞评估（VA）报告的验证过程。这项研究的关键发现是，LLMs能够有效减少误报并提高验证效率，为提升网络安全策略和工具提供了实证支持。

网络安全攻势操作中的语言模型武器化：自动化漏洞评估报告验证；综述论文