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Software vulnerabilities are among the significant causes of security breaches. Vulnerabilities can severely compromise software security if exploited by malicious attacks and may result in catastrophic losses. Hence, Automatic vulnerability detection methods promise to mitigate attack risks and safeguard software security. This paper introduces a novel model for automatic vulnerability detection of source code vulnerabilities dubbed DB-CBIL using a hybrid deep learning model based on Distilled Bidirectional Encoder Representations from Transformers (DistilBERT). The proposed model considers contextualized word embeddings using the language model for the syntax and semantics of source code functions based on the Abstract Syntax Tree (AST) representation. The model includes two main phases. First, using a vulnerable code dataset, the pre-trained DistilBert transformer is fine-tuned for word embedding …