# SafeGenBench: A Benchmark Framework for Security Vulnerability Detection in LLM-Generated Code

arXiv: https://arxiv.org/abs/2506.05692 · HTML: https://mlsanity.com/paper/2506.05692/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 29 references that are in the map.
- First posted on arXiv 6 Jun 2025.

## Builds on (most cited first, 18 of 18)

- [Program Synthesis with Large Language Models](https://mlsanity.com/paper/2108.07732/) (2021)
- [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023)
- [LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code](https://mlsanity.com/paper/2403.07974/) (2024)
- [Competition-Level Code Generation with AlphaCode](https://mlsanity.com/paper/2203.07814/) (2022)
- [A Survey on LLM-as-a-Judge](https://mlsanity.com/paper/2411.15594/) (2024)
- [Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation](https://mlsanity.com/paper/2305.01210/) (2023)
- [Measuring Coding Challenge Competence With APPS](https://mlsanity.com/paper/2105.09938/) (2021)
- [StarCoder 2 and The Stack v2: The Next Generation](https://mlsanity.com/paper/2402.19173/) (2024)
- [DS-1000: A Natural and Reliable Benchmark for Data Science Code Generation](https://mlsanity.com/paper/2211.11501/) (2022)
- [Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code Contributions](https://mlsanity.com/paper/2108.09293/) (2021)
- [CrossCodeEval: A Diverse and Multilingual Benchmark for Cross-File Code Completion](https://mlsanity.com/paper/2310.11248/) (2023)
- [MarsCode Agent: AI-native Automated Bug Fixing](https://mlsanity.com/paper/2409.00899/) (2024)
- [CWEval: Outcome-driven Evaluation on Functionality and Security of LLM Code Generation](https://mlsanity.com/paper/2501.08200/) (2025)
- [CodeLMSec Benchmark: Systematically Evaluating and Finding Security Vulnerabilities in Black-Box Code Language Models](https://mlsanity.com/paper/2302.04012/) (2023)
- [Investigating Large Language Models for Code Vulnerability Detection: An Experimental Study](https://mlsanity.com/paper/2412.18260/) (2024)
- [An Extensive Comparison of Static Application Security Testing Tools](https://mlsanity.com/paper/2403.09219/) (2024)
- [LLMSecCode: Evaluating Large Language Models for Secure Coding](https://mlsanity.com/paper/2408.16100/) (2024)
- [Vulnerabilities Mapping based on OWASP-SANS: a Survey for Static Application Security Testing (SAST)](https://mlsanity.com/paper/2004.03216/) (2020)

## Built on by (most cited first, 19 of 19)

- [A.S.E: A Repository-Level Benchmark for Evaluating Security in AI-Generated Code](https://mlsanity.com/paper/2508.18106/) (2025)
- [SecureVibeBench: Benchmarking Secure Vibe Coding of AI Agents via Reconstructing Vulnerability-Introducing Scenarios](https://mlsanity.com/paper/2509.22097/) (2025)
- [HardSecBench: Benchmarking the Security Awareness of LLMs for Hardware Code Generation](https://mlsanity.com/paper/2601.13864/) (2026)
- [TriggerBench: Investigating Prospective Memory for Large Language Models](https://mlsanity.com/paper/2606.23459/) (2026)
- [When "Correct" Is Not Safe: Can We Trust Functionally Correct Patches Generated by Code Agents?](https://mlsanity.com/paper/2510.17862/) (2025)
- [Scam2Prompt: A Scalable Framework for Auditing Malicious Scam Endpoints in Production LLMs](https://mlsanity.com/paper/2509.02372/) (2025)
- [Accelerating Scientific Research with Gemini in the Real-World](https://mlsanity.com/paper/2608.26701/) (2026)
- [DualGauge: Automated Joint Security-Functionality Benchmarking of Specification-Only Code Generation by LLMs and Coding Agents](https://mlsanity.com/paper/2511.20709/) (2025)
- [Extracting Recurring Vulnerabilities from Black-Box LLM-Generated Software](https://mlsanity.com/paper/2602.04894/) (2026)
- [SecCodeBench-V2 Technical Report](https://mlsanity.com/paper/2602.15485/) (2026)
- [TOSSS: a CVE-based Software Security Benchmark for Large Language Models](https://mlsanity.com/paper/2603.10969/) (2026)
- [FinRED: An Expert-Guided Benchmark Generation and Evaluation Framework for Financial LLM Red-Teaming](https://mlsanity.com/paper/2606.19887/) (2026)
- [VulReaD: Knowledge-Graph-guided Software Vulnerability Reasoning and Detection](https://mlsanity.com/paper/2602.10787/) (2026)
- [CoT-Guard: Small Models for Strong Monitoring](https://mlsanity.com/paper/2605.12746/) (2026)
- [ASSEMBLAGE-DEEPHISTORY: A Cross-Build Binary Dataset with Temporal Coverage](https://mlsanity.com/paper/2605.21615/) (2026)
- [An Empirical Study of Security Calibration in Large Language Models for Code](https://mlsanity.com/paper/2606.31159/) (2026)
- [The Patchwork Problem in LLM-Generated Code](https://mlsanity.com/paper/2607.08981/) (2026)
- [Adversarial Testing of Automated Program Repair Agents for Security Vulnerabilities](https://mlsanity.com/paper/2609.15963/) (2026)
- [Security in LLM-Assisted Coding: A Systematic Literature Review](https://mlsanity.com/paper/2610.15482/) (2026)

## Abstract (arXiv)

The code generation capabilities of large language models(LLMs) have emerged as a critical dimension in evaluating their overall performance. However, prior research has largely overlooked the security risks inherent in the generated code. In this work, we introduce SafeGenBench, a benchmark specifically designed to assess the security of LLM-generated code. The dataset encompasses a wide range of common software development scenarios and vulnerability types. Building upon this benchmark, we develop an automatic evaluation framework that leverages both static application security testing(SAST) and LLM-based judging to assess the presence of security vulnerabilities in model-generated code. Through the empirical evaluation of state-of-the-art LLMs on SafeGenBench, we reveal notable deficiencies in their ability to produce vulnerability-free code. Our findings highlight pressing challenges and offer actionable insights for future advancements in the secure code generation performance of LLMs. The data and code will be released soon.