用更快的代码预处理提升大模型漏洞检测效率
Enhancing Large Language Models with Faster Code Preprocessing for Vulnerability Detection
- 改进代码标准化工具SCoPE2，统一代码表达形式
- 处理时间减少97.3%，大模型检测F1分数提升
- 适合需要高效漏洞检测的开发与安全团队
人工智能在软件漏洞检测中展现出强大能力，但有效检测依赖于准确捕捉代码语义结构与上下文关系。由于相同功能可有多种实现方式，代码表示的标准化至关重要。为此，我们基于现有SCoPE框架构建了性能更优的SCoPE2，该工具具备更高效率、跨语言适应性及新转换支持能力。我们在处理时间与内存占用方面对比了SCoPE与SCoPE2，并评估其对大型语言模型（LLM）漏洞检测的影响。结果表明，使用SCoPE2使处理时间降低97.3%，且仅因预处理优化，LLM的F1得分即得到提升。
原文摘要 · Abstract (English)
The application of Artificial Intelligence has become a powerful approach to detecting software vulnerabilities. However, effective vulnerability detection relies on accurately capturing the semantic structure of code and its contextual relationships. Given that the same functionality can be implemented in various forms, a preprocessing tool that standardizes code representation is important. This tool must be efficient, adaptable across programming languages, and capable of supporting new transformations. To address this challenge, we build on the existing SCoPE framework and introduce SCoPE2, an enhanced version with improved performance. We compare both versions in terms of processing time and memory usage and evaluate their impact on a Large Language Model (LLM) for vulnerability detection. Our results show a 97.3\% reduction in processing time with SCoPE2, along with an improved F1-score for the LLM, solely due to the refined preprocessing approach.
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