Poster: The Concept of a System for Automatic Detection and Correction of Vulnerabilities in the Source Code
Open Access
- 2 December 2024
- conference paper
- Published by Association for Computing Machinery (ACM)
- p. 4943-4945
Abstract
Defects in the source code that affect security are one of the main elements used to carry out cyber attacks. Examining source code for vulnerabilities is a difficult and expensive process. As a result, specialized software is needed for this. Due to the development of various artificial intelligence methods, improving existing vulnerability detection methods is possible. In particular, it is possible to reduce the number of false positives and enable the detection of complex vulnerabilities that require understanding the broader context of the code. The article presents the concept of a system for automatic analysis of vulnerabilities in source code, along with the challenges and problems related to its design and use.Keywords
This publication has 9 references indexed in Scilit:
- GRACE: Empowering LLM-based software vulnerability detection with graph structure and in-context learningJournal of Systems and Software, 2024
- Vulnerability Detection and Monitoring Using LLM2023
- Software Vulnerability Detection using Large Language Models2023
- Examining Zero-Shot Vulnerability Repair with Large Language Models2023
- Machine Learning for Source Code Vulnerability Detection: What Works and What Isn’t There YetIEEE Security & Privacy, 2022
- A deep learning‐based approach for software vulnerability detection using code metricsIET Software, 2022
- A systematic evaluation of large language models of code2022
- Automatically Detect Software Security Vulnerabilities Based on Natural Language Processing Techniques and Machine Learning AlgorithmsJournal of ICT Research and Applications, 2022
- An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine TranslationACM Transactions on Software Engineering and Methodology, 2019