Researchers Release RealSec-bench to Measure Secure Code Generation by Large Language Models

Global: Researchers Release RealSec-bench to Measure Secure Code Generation by Large Language Models

In a 2026 preprint, a team of computer science researchers introduced RealSec-bench, a benchmark designed to evaluate how well large language models (LLMs) generate secure Java code. The study assessed five widely used LLMs, applying a new composite metric called SecurePass@K that jointly measures functional correctness and security. The work aims to address shortcomings in existing code‑generation benchmarks, which often rely on synthetic vulnerabilities or isolate functionality from security concerns.

Benchmark Construction

The authors assembled RealSec-bench by extracting high‑risk code snippets from real‑world Java repositories. They employed a multi‑stage pipeline that began with systematic static application security testing (SAST) using CodeQL, followed by LLM‑assisted filtering of false positives, and concluded with validation by human security experts.

Dataset Characteristics

The final benchmark comprises 105 instances drawn from diverse repository contexts, covering 19 distinct Common Weakness Enumeration (CWE) categories. Some vulnerabilities exhibit complex data‑flow patterns, including inter‑procedural dependencies spanning up to 34 hops, thereby reflecting realistic security challenges.

Evaluation Methodology

To gauge model performance, the researchers prompted each LLM to generate code for the benchmark tasks and then evaluated the outputs with SecurePass@K, which rewards solutions that both compile correctly and avoid the identified weaknesses. The study also examined the impact of retrieval‑augmented generation (RAG) and the inclusion of generic security guidelines in prompts.

Key Findings

Results indicated that while RAG techniques modestly improved functional correctness, they yielded negligible gains in security outcomes. Moreover, prompting models with broad security instructions frequently caused compilation errors, reducing functional correctness without consistently eliminating vulnerabilities.

Implications for Future Research

The authors conclude that current LLMs exhibit a notable disparity between generating functional code and producing secure code, highlighting a need for dedicated security‑focused training and evaluation frameworks. They suggest that future work explore more targeted prompting strategies and model architectures that integrate security reasoning.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.