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Enhancing Python Code Security: A Comparison of Machine Learning, ChatGPT, and Static Analysis Methods
0
Zitationen
2
Autoren
2025
Jahr
Abstract
Detecting software vulnerabilities in Python code is crucial for maintaining application security. This paper presents a comparative study of static analysis tools, a machine learning model, and the large language model ChatGPT for vulnerability detection in Python source code. We evaluate these approaches on 21 Python scripts representing seven common vulnerability types. Our results show that both the machine learning model and ChatGPT significantly outperform traditional static analyzers. This work highlights the promising potential of leveraging advanced data-driven methods and large language models to improve Python vulnerability detection.
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