@inproceedings{lian-etal-2026-e,
title = "{A}.{S}.{E}: A Repository-Level Benchmark for Evaluating Security in {AI}-Generated Code",
author = "Lian, Keke and
Bin, Wang and
Zhang, Lei and
Chen, Libo and
Wang, Junjie and
Zhao, Ziming and
Yang, Yujiu and
Lin, Miaoqian and
Duan, Haotong and
Zhao, Haoran and
Liao, Shuang and
Guo, Mingda and
Jiazheng, Quan and
Zhong, Yilu and
He, Chenhao and
Zichuan, Chen and
Wu, Jie and
Li, Haoling and
Li, Zhaoxuan and
Yu, Jiongchi and
LI, Hui and
Zhang, Dong",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1569/",
pages = "31390--31405",
ISBN = "979-8-89176-395-1",
abstract = "The increasing adoption of large language models (LLMs) in software engineering necessitates rigorous security evaluation of their generated code. However, existing benchmarks often lack relevance to real-world AI-assisted programming scenarios, making them inadequate for assessing the practical security risks associated with AI-generated code in production environments. To address this gap, we introduce A.S.E (AI Code Generation Security Evaluation), a repository-level evaluation benchmark designed to closely mirror real-world AI programming tasks, offering a comprehensive and reliable framework for assessing the security of AI-generated code. Our evaluation of leading LLMs on A.S.E reveals several key findings. In particular, current LLMs still struggle with secure coding. The complexity in repository-level scenarios presents challenges for LLMs that typically perform well on snippet-level tasks. Moreover, a larger reasoning budget does not necessarily lead to better code generation. These observations offer valuable insights into the current state of AI code generation and help developers identify the most suitable models for practical tasks. They also lay the groundwork for refining LLMs to generate secure and efficient code in real-world applications."
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<abstract>The increasing adoption of large language models (LLMs) in software engineering necessitates rigorous security evaluation of their generated code. However, existing benchmarks often lack relevance to real-world AI-assisted programming scenarios, making them inadequate for assessing the practical security risks associated with AI-generated code in production environments. To address this gap, we introduce A.S.E (AI Code Generation Security Evaluation), a repository-level evaluation benchmark designed to closely mirror real-world AI programming tasks, offering a comprehensive and reliable framework for assessing the security of AI-generated code. Our evaluation of leading LLMs on A.S.E reveals several key findings. In particular, current LLMs still struggle with secure coding. The complexity in repository-level scenarios presents challenges for LLMs that typically perform well on snippet-level tasks. Moreover, a larger reasoning budget does not necessarily lead to better code generation. These observations offer valuable insights into the current state of AI code generation and help developers identify the most suitable models for practical tasks. They also lay the groundwork for refining LLMs to generate secure and efficient code in real-world applications.</abstract>
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%0 Conference Proceedings
%T A.S.E: A Repository-Level Benchmark for Evaluating Security in AI-Generated Code
%A Lian, Keke
%A Bin, Wang
%A Zhang, Lei
%A Chen, Libo
%A Wang, Junjie
%A Zhao, Ziming
%A Yang, Yujiu
%A Lin, Miaoqian
%A Duan, Haotong
%A Zhao, Haoran
%A Liao, Shuang
%A Guo, Mingda
%A Jiazheng, Quan
%A Zhong, Yilu
%A He, Chenhao
%A Zichuan, Chen
%A Wu, Jie
%A Li, Haoling
%A Li, Zhaoxuan
%A Yu, Jiongchi
%A LI, Hui
%A Zhang, Dong
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F lian-etal-2026-e
%X The increasing adoption of large language models (LLMs) in software engineering necessitates rigorous security evaluation of their generated code. However, existing benchmarks often lack relevance to real-world AI-assisted programming scenarios, making them inadequate for assessing the practical security risks associated with AI-generated code in production environments. To address this gap, we introduce A.S.E (AI Code Generation Security Evaluation), a repository-level evaluation benchmark designed to closely mirror real-world AI programming tasks, offering a comprehensive and reliable framework for assessing the security of AI-generated code. Our evaluation of leading LLMs on A.S.E reveals several key findings. In particular, current LLMs still struggle with secure coding. The complexity in repository-level scenarios presents challenges for LLMs that typically perform well on snippet-level tasks. Moreover, a larger reasoning budget does not necessarily lead to better code generation. These observations offer valuable insights into the current state of AI code generation and help developers identify the most suitable models for practical tasks. They also lay the groundwork for refining LLMs to generate secure and efficient code in real-world applications.
%U https://aclanthology.org/2026.findings-acl.1569/
%P 31390-31405
Markdown (Informal)
[A.S.E: A Repository-Level Benchmark for Evaluating Security in AI-Generated Code](https://aclanthology.org/2026.findings-acl.1569/) (Lian et al., Findings 2026)
ACL
- Keke Lian, Wang Bin, Lei Zhang, Libo Chen, Junjie Wang, Ziming Zhao, Yujiu Yang, Miaoqian Lin, Haotong Duan, Haoran Zhao, Shuang Liao, Mingda Guo, Quan Jiazheng, Yilu Zhong, Chenhao He, Chen Zichuan, Jie Wu, Haoling Li, Zhaoxuan Li, Jiongchi Yu, Hui LI, and Dong Zhang. 2026. A.S.E: A Repository-Level Benchmark for Evaluating Security in AI-Generated Code. In Findings of the Association for Computational Linguistics: ACL 2026, pages 31390–31405, San Diego, California, United States. Association for Computational Linguistics.