SecureQwen: Leveraging LLMs for vulnerability detection in python codebases - Mechri, Abdechakour; Ferrag, Mohamed Amine; Debbah, Merouane (Computers & security, 2025)
Identifying vulnerabilities in software code is crucial for ensuring the security of modern systems. However, manual detection requires expert knowledge and is time-consuming, underscoring the need for automated techniques. In this paper, we present SecureQwen, a novel vulnerability detection tool leveraging large language models (LLMs) with a context length of 64K tokens to identify potential security threats in large-scale Python codebases. Utilizing a decoder-only transformer architecture, SecureQwen captures complex relationships between code tokens, enabling accurate classification of vulnerable code sequences across 14 common weakness enumerations (CWEs), including OS Command Injection, SQL Injection, Improper Check or Handling of Exceptional Conditions, Path Traversal, Broken or Risky Cryptographic Algorithm, Deserialization of Untrusted Data, and Cleartext Transmission of Sensitive Information. Therefore, we evaluate SecureQwen on a large Python dataset with over 1.875 million function-level code snippets from different sources, including GitHub repositories, Codeparrot’s dataset, and synthetic data generated by GPT4-o. The experimental evaluation demonstrates high accuracy, with F1 scores ranging from 84% to 99%. The results indicate that SecureQwen effectively detects vulnerabilities in human-written and AI-generated code.