# Persistent Human Feedback, LLMs, and Static Analyzers for Secure Code Generation and Vulnerability Detection

arXiv: https://arxiv.org/abs/2602.05868 · HTML: https://mlsanity.com/paper/2602.05868/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Feb 2026.

## Built on by (most cited first, 5 of 5)

- [Rule Taxonomy and Evolution in AI IDEs: A Mining and Survey Study](https://mlsanity.com/paper/2606.12231/) (2026)
- [Revelio: Cost-Efficient Agentic Memory Safety Vulnerability Detection For Repository-Scale Codebases](https://mlsanity.com/paper/2606.22263/) (2026)
- [Activating Latent Security Knowledge through LLM-Guided Risk Analysis for Secure Code Generation](https://mlsanity.com/paper/2606.16244/) (2026)
- [Large Language Models at the Intersection of Software Engineering and Software Security:An Evidence-Centered Structured Survey and Research Agenda](https://mlsanity.com/paper/2608.21107/) (2026)
- [Security in LLM-Assisted Coding: A Systematic Literature Review](https://mlsanity.com/paper/2610.15482/) (2026)

## Abstract (arXiv)

Existing literature heavily relies on static analysis tools to evaluate LLMs for secure code generation and vulnerability detection. We reviewed 1,080 LLM-generated code samples, built a human-validated ground-truth, and compared the outputs of two widely used static security tools, CodeQL and Semgrep, against this corpus. While 61% of the samples were genuinely secure, Semgrep and CodeQL classified 60% and 80% as secure, respectively. Despite the apparent agreement in aggregate statistics, per-sample analysis reveals substantial discrepancies: only 65% of Semgrep's and 61% of CodeQL's reports correctly matched the ground truth. These results question the reliability of static analysis tools as sole evaluators of code security and underscore the need for expert feedback. Building on this insight, we propose a conceptual framework that persistently stores human feedback in a dynamic retrieval-augmented generation pipeline, enabling LLMs to reuse past feedback for secure code generation and vulnerability detection.