SPVR: syntax-to-prompt vulnerability repair based on large language models

Understand

Purpose: In the field of vulnerability repair, previous research has leveraged pretrained models and LLM-based prompt engineering, among which LLM-based approaches show better generalizability and achieve the best performance.

• However, the LLM-based approaches generally regard vulnerability repair as a sequence-to-sequence task, and do not explicitly capture the syntax patterns for different vulnerability types, leading to limited accuracy.
• We aim to create a method that ensures the specificity of prompts targeting vulnerable code while also leveraging the generative capabilities of Large Language Models.
• Methods: We propose SPVR (Syntax-to-Prompt Vulnerability Repair), a novel framework that collects information from syntax trees, and generates corresponding prompts.