# Enhancing Large Language Models with Faster Code Preprocessing for Vulnerability Detection

arXiv: https://arxiv.org/abs/2505.05600 · HTML: https://mlsanity.com/paper/2505.05600/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 20 references that are in the map.
- First posted on arXiv 8 May 2025.

## Builds on (most cited first, 11 of 11)

- [DiverseVul: A New Vulnerable Source Code Dataset for Deep Learning Based Vulnerability Detection](https://mlsanity.com/paper/2304.00409/) (2023)
- [Adversarial Examples for Models of Code](https://mlsanity.com/paper/1910.07517/) (2019)
- [Data Quality for Software Vulnerability Datasets](https://mlsanity.com/paper/2301.05456/) (2023)
- [Finetuning Large Language Models for Vulnerability Detection](https://mlsanity.com/paper/2401.17010/) (2024)
- [Harnessing Large Language Models for Software Vulnerability Detection: A Comprehensive Benchmarking Study](https://mlsanity.com/paper/2405.15614/) (2024)
- [How Important are Good Method Names in Neural Code Generation? A Model Robustness Perspective](https://mlsanity.com/paper/2211.15844/) (2022)
- [Comparison between parameter-efficient techniques and full fine-tuning: A case study on multilingual news article classification](https://mlsanity.com/paper/2308.07282/) (2023)
- [An Extensive Study on Adversarial Attack against Pre-trained Models of Code](https://mlsanity.com/paper/2311.07553/) (2023)
- [Harnessing the Power of LLMs in Source Code Vulnerability Detection](https://mlsanity.com/paper/2408.03489/) (2024)
- [SCoPE: Evaluating LLMs for Software Vulnerability Detection](https://mlsanity.com/paper/2407.14372/) (2024)
- [Evaluating LLaMA 3.2 for Software Vulnerability Detection](https://mlsanity.com/paper/2503.07770/) (2025)

## Abstract (arXiv)

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.