Gradio, a popular Python web framework for showcasing machine learning applications, has been examined for vulnerabilities using CodeQL, a static analysis tool. The process involved modeling Gradio's input components as potential sources of vulnerabilities, notably in applications using the gr.Interface and gr.Blocks classes. By tracking data flows from these sources to potentially unsafe operations, several security issues were identified, including command injection vulnerabilities. The findings highlight the importance of understanding how user inputs in Gradio can be misused if not properly sanitized. While the vulnerabilities have been addressed in Gradio version 5.0, the research underscores the necessity of continuous security assessments, especially for applications built on earlier versions. The analysis also demonstrates the application of CodeQL's taint tracking to scale vulnerability detection across multiple projects, showcasing the tool's utility in enhancing open-source software security.