Cross-Domain Transferability of Vulnerability Classification Models via Binary Code Similarity

This report synthesises findings from 12 peer-reviewed papers addressing the following research question: What is the cross-domain transferability of vulnerability classification models trained on binary code similarity analysis tasks, as evaluated by accuracy degradation when applied to unseen. 9 claims were extracted from source literature; 8 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.5/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: What is the cross-domain transferability of vulnerability classification models trained on binary code similarity analysis tasks, as evaluated by accuracy degradation when applied to unseen programming languages or architectures?

Autonomous literature synthesis. Automated review score: 8.5/10. Full text and citation available at Assignee Research.