Enhance Language Model-based Repair for Memory-related Vulnerabilities via Knowledge-and Semantic-guided Analysis

Memory-related vulnerabilities often result in system crashes and performance drops, imposing significant risks for embedded systems. Despite the potential of Language Models (LMs) in program repair, existing LM-based approaches struggle with these vulnerabilities due to two primary limitations: i) LMs do not possess adequate domain knowledge concerning program analysis and the characteristics of memory-related vulnerabilities, and ii) LMs face constraints in managing contexts as the size of programs increases. To address this issue, we introduce MVRepair, a novel lightweight Language Model (ℓLM)-driven framework built upon a domain-specific knowledge library that is developed through the examination of 7,935 real-world memory-related vulnerabilities. By using our proposed knowledge-based analysis strategy and semantic-guided segmentation mechanism, MVRepair can substantially enhance the LM's ability to repair programs with memory-related vulnerabilities. Comprehensive experimental results on 8,118 real-world memory-related vulnerabilities demonstrate that, compared with state-of-the-art LM-based approaches, MVRepair yields improvements of a minimum of 23.8% in EM, 31.9% in BLEU-4, and 16.7% in CodeBLEU.