2026 Project
VulnSeeker: CodeQL and LLM Security Analysis
An automated security analysis pipeline that combines CodeQL static analysis and LLM-based vulnerability judgement.
VulnSeeker explores how static program analysis and LLM reasoning can be combined for automated security assessment.
Key components:
- CodeQL-based vulnerability detection;
- automated expansion of relevant external function context;
- prompt workflows for CodeQL and LLM interaction;
- structured security judgement states;
- combined assessment from static rules and model classification.
This project should be highlighted for AI security applications because it connects research ideas with a practical code-analysis workflow.