# Machine Learning Techniques for Python Source Code Vulnerability Detection

arXiv: https://arxiv.org/abs/2404.09537 · HTML: https://mlsanity.com/paper/2404.09537/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 14 references that are in the map.
- First posted on arXiv 15 Apr 2024.

## Builds on (most cited first, 6 of 6)

- [Automated Vulnerability Detection in Source Code Using Deep Representation Learning](https://mlsanity.com/paper/1807.04320/) (2018)
- [SySeVR: A Framework for Using Deep Learning to Detect Software Vulnerabilities](https://mlsanity.com/paper/1807.06756/) (2018)
- [VUDENC: Vulnerability Detection with Deep Learning on a Natural Codebase for Python](https://mlsanity.com/paper/2201.08441/) (2022)
- [Mining Fix Patterns for FindBugs Violations](https://mlsanity.com/paper/1712.03201/) (2017)
- [Learning Quick Fixes from Code Repositories](https://mlsanity.com/paper/1803.03806/) (2018)
- [A Comparison of Different Source Code Representation Methods for Vulnerability Prediction in Python](https://mlsanity.com/paper/2108.02044/) (2021)

## Abstract (arXiv)

Software vulnerabilities are a fundamental reason for the prevalence of cyber attacks and their identification is a crucial yet challenging problem in cyber security. In this paper, we apply and compare different machine learning algorithms for source code vulnerability detection specifically for Python programming language. Our experimental evaluation demonstrates that our Bidirectional Long Short-Term Memory (BiLSTM) model achieves a remarkable performance (average Accuracy = 98.6%, average F-Score = 94.7%, average Precision = 96.2%, average Recall = 93.3%, average ROC = 99.3%), thereby, establishing a new benchmark for vulnerability detection in Python source code.