Securing AI-Generated Code: A Just-in-Time Vulnerability Detection and Remediation Pipeline
AI-assisted development tools generate vulnerable code at significant rates, yet few automated mechanisms exist to detect, enrich, fix, and verify security issues at development velocity, particularly ones that ground remediation in real-world threat context. This paper presents an automated security evaluation pipeline that generates Python code from LLMSecEval prompts, scans for vulnerabilities using CodeQL and Bandit in parallel with an independent Code Validator LLM, enriches the Code Validator findings with MITRE ATT&CK techniques, CWE Observed Examples, and Python best practice guidelines, generates fixes via the Code Generation LLM, and re-scans with CodeQL and Bandit to verify out...
| Authors | Mikhail Surikov |
|---|---|
| Primary category | cs.CR |
| Published | 17 August 2026 |
| Last updated at source | 17 August 2026 |
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