This research examines the security vulnerabilities and quality issues in code generated by Large Language Models across multiple programming languages.

• Evaluated 200 tasks across six programming categories to assess LLM code generation security
• Analyzed multiple popular LLMs to identify common security weaknesses
• Found significant variation in security performance across different programming languages
• Results highlight the need for enhanced security validation of AI-generated code in development workflows

For businesses integrating AI coding assistants, this research provides critical insights into potential security risks and the importance of human review before deployment.

Security and Quality in LLM-Generated Code: A Multi-Language, Multi-Model Analysis