What is the comparative impact of syntax-aware text preprocessing on the false positive rates of Llama3, Codes

Abstract The rapid development of large language models (LLMs) has opened new avenues across various fields, including cybersecurity, which faces an evolving threat landscape and demand for innovative technologies. Despite initial explorations into the application of LLMs in cybersecurity, there is a lack of a comprehensive overview of this research area. This paper addresses this gap by providing a systematic literature review, covering the analysis of over 300 works, encompassing 25 LLMs and more than 10 downstream scenarios. Our comprehensive overview addresses three key research questions:

Research goal: What is the comparative impact of syntax-aware text preprocessing on the false positive rates of Llama3, Codestral, and Deepseek R1 when evaluating security vulnerabilities in diverse programming languages?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 9.0/10.