  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.
