🤖 AI Summary
This study addresses the high false-positive rate and low manual verification efficiency in vulnerability assessment (VA) reports. We propose and implement, for the first time, an LLM-driven paradigm for automatic VA report validation. Methodologically, we integrate critical literature review, semantic parsing of VA reports, and credibility reasoning to construct an LLM-based verification framework tailored to offensive security contexts. Unlike conventional rule-based or statistical models, our approach enables deep discrimination of technical vulnerability details, contextual consistency, and evidentiary sufficiency. Experimental results demonstrate a 37.2% average reduction in false positives, a 5.8× improvement in verification throughput, and an accuracy of 92.4%, substantially reducing human effort. This work bridges a critical research gap—applying LLMs to trustworthy, evidence-grounded validation in offensive cybersecurity tasks.
📝 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.