Benchmarking Large Language Models for Multi-Language Software
  Vulnerability Detection
Authors: name: Ting Zhang, name: Chengran Yang, name: Yindu Su, name: Martin Weyssow, name: Hung Nguyen, name: Tan Bui, name: Hong Jin Kang, name: Yikun Li, name: Eng Lieh Ouh, name: Lwin Khin Shar, name: David Lo

Recent advancements in generative AI have led to the widespread adoption of
large language models (LLMs) in software engineering, addressing numerous
long-standing challenges. However, a comprehensive study examining the
capabilities of LLMs in software vulnerability detection (SVD), a crucial
aspect of software security, is currently lacking. Existing research primarily
focuses on evaluating LLMs using C/C++ datasets. It typically explores only one
or two strategies among prompt engineering, instruction tuning, and sequence
classification fine-tuning for open-source LLMs. Consequently, there is a
significant knowledge gap regarding the effectiveness of diverse LLMs in
detecting vulnerabilities across various programming languages. To address this
knowledge gap, we present a comprehensive empirical study evaluating the
performance of LLMs on the SVD task. We have compiled a comprehensive dataset
comprising 8,260 vulnerable functions in Python, 7,505 in Java, and 28,983 in
JavaScript. We assess five open-source LLMs using multiple approaches,
including prompt engineering, instruction tuning, and sequence classification
fine-tuning. These LLMs are benchmarked against five fine-tuned small language
models and two open-source static application security testing tools.
Furthermore, we explore two avenues to improve LLM performance on SVD: a) Data
perspective: Retraining models using downsampled balanced datasets. b) Model
perspective: Investigating ensemble learning methods that combine predictions
from multiple LLMs. Our comprehensive experiments demonstrate that SVD remains
a challenging task for LLMs. This study provides a thorough understanding of
the role of LLMs in SVD and offers practical insights for future advancements
in leveraging generative AI to enhance software security practices.