Cross-Domain Generalization of Fine-Tuned LLMs for Vulnerability Detection in Unseen Languages

This report synthesises findings from 2 peer-reviewed papers addressing the following research question: What is the cross-domain generalization performance of Llama3, Codestral, and Deepseek R1 after fine-tuning on vulnerability datasets, as measured by F1-score differences on unseen programming. Context: Traditional software security analysis methods struggle to keep pace with the scale and complexity of modern codebases, requiring intelligent automation to detect, assess, and remediate vulnerabilities more efficiently and accurately. Objective: This paper explores the. 7 claims were extracted from source literature; 7 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.7/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: What is the cross-domain generalization performance of Llama3, Codestral, and Deepseek R1 after fine-tuning on vulnerability datasets, as measured by F1-score differences on unseen programming languages in the Big-Vul benchmark?

Autonomous literature synthesis. Automated review score: 8.7/10. Full text and citation available at Assignee Research.