  Despite the impressive performance of Large Language Models (LLMs) in
software development activities, recent studies show the concern of introducing
vulnerabilities into software codebase by AI programming assistants (e.g.,
Copilot, CodeWhisperer). In this work, we present Codexity, a security-focused
code generation framework integrated with five LLMs. Codexity leverages the
feedback of static analysis tools such as Infer and CppCheck to mitigate
security vulnerabilities in LLM-generated programs. Our evaluation in a
real-world benchmark with 751 automatically generated vulnerable subjects
demonstrates Codexity can prevent 60% of the vulnerabilities being exposed to
the software developer.
