Russell, R., Kim, L., Hamilton, L., Lazovich, T., Harer, J., Ozdemir, O., et al. (2018) Automated Vulnerability Detection in Source Code Using Deep Representation Learning. 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando, 17-20 December 2018, 757-762.
https://doi.org/10.1109/icmla.2018.00120

has been cited by the following article:

• TITLE: Large Language Models (LLMs) for Software Security Analysis

  AUTHORS: Brian Mata, Vijay Madisetti

  KEYWORDS: Large Language Models, Software Security

  JOURNAL NAME: Journal of Information Security, Vol.16 No.2, April 29, 2025

  ABSTRACT: Security vulnerabilities are a widespread and costly aspect of software engineering. Although tools exist to detect these vulnerabilities, non-machine learning techniques are often rigid and unable to detect many types of vulnerabilities, while machine learning techniques often struggle with large codebases. Recent work has aimed to combine traditional static analysis with machine learning. Our work enhances this by equipping LLM-based agents with classic static analysis tools, leveraging the strengths of both methods while addressing their inherent weaknesses. We achieved a false detection rate of 0.5696, significantly improving over the previous state-of-the-art LLM-enabled technique, IRIS, which has a false detection rate of 0.8482. Furthermore, using Claude Sonnet 3.5, our technique produces an F1 score of 0.1281, which is an improvement over the standard CodeQL suite and approaches IRIS’s score of 0.1770.