{"id":43705900,"url":"https://github.com/Tencent/AICGSecEval","last_synced_at":"2026-02-16T16:00:33.830Z","repository":{"id":304587698,"uuid":"1015741629","full_name":"Tencent/AICGSecEval","owner":"Tencent","description":"A.S.E (AICGSecEval) is a repository-level AI-generated code security evaluation benchmark developed by Tencent Wukong Code Security 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unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["agent","aigc","benchmark","codesecurity","llm"],"created_at":"2026-02-05T06:00:17.290Z","updated_at":"2026-02-16T16:00:33.824Z","avatar_url":"https://github.com/Tencent.png","language":"Python","readme":"\u003cp align=\"center\"\u003e\n    \u003ch1 align=\"center\"\u003e\u003cimg vertical-align=“middle” width=\"400px\" src=\"img/title_header.png\" alt=\"A.S.E\"/\u003e\u003c/h1\u003e\n\u003c/p\u003e\n\n\u003ch4 align=\"center\"\u003e\n    \u003cp\u003e\n        \u003c!-- \u003ca href=\"https://tencent.github.io/xxxx/\"\u003eDocumentation\u003c/a\u003e | --\u003e\n        \u003ca href=\"./README_zh.md\"\u003e中文\u003c/a\u003e |\n        \u003ca href=\"#\"\u003eEnglish\u003c/a\u003e\n    \u003cp\u003e\n\u003c/h4\u003e\n\n\u003cp align=\"center\"\u003e\n  \u003ca href=\"https://github.com/Tencent/AICGSecEval\"\u003e\n        \u003cimg alt=\"Release\" src=\"https://img.shields.io/github/v/release/Tencent/AICGSecEval?color=green\"\u003e\n    \u003c/a\u003e\n    \u003ca href=\"https://github.com/Tencent/AICGSecEval\"\u003e\n        \u003cimg alt=\"GitHub Stars\" src=\"https://img.shields.io/github/stars/Tencent/AICGSecEval?color=gold\"\u003e\n    \u003c/a\u003e\n    \u003ca href=\"https://github.com/Tencent/AICGSecEval\"\u003e\n        \u003cimg alt=\"GitHub Stars\" src=\"https://img.shields.io/github/forks/Tencent/AICGSecEval?color=gold\"\u003e\n    \u003c/a\u003e\n    \u003c!-- \u003ca href=\"https://github.com/Tencent/AICGSecEval\"\u003e\n        \u003cimg alt=\"GitHub downloads\" src=\"https://img.shields.io/github/downloads/Tencent/AICGSecEval/total\"\u003e\n    \u003c/a\u003e --\u003e\n\u003c/p\u003e\n\n\n\u003cbr\u003e\n\u003cp align=\"center\"\u003e\n    \u003ch3 align=\"center\"\u003e🚀 Repository-level AI-generated Code Security Evaluation Framework by \u003cbr\u003e「Tencent Wukong Code Security Team」\u003c/h3\u003e\n\u003c/p\u003e\n\n\n**A.S.E (AICGSecEval)** provides a **project-level benchmark for evaluating the security of AI-generated code**, designed to assess the security performance of AI-assisted programming by simulating real-world development workflows:  \n* **Code Generation Tasks** – Derived from real-world GitHub projects and authoritative CVE patches, ensuring both practical relevance and security sensitivity.  \n* **Code Generation Process** – Automatically extracts project-level code context to accurately simulate realistic AI programming scenarios.  \n* **Code Security Evaluation** – Integrates a hybrid evaluation suite combining static and dynamic analysis, balancing detection coverage and verification precision to enhance the scientific rigor and practical value of security assessments.\n\n\n\u003cp align=\"center\"\u003e\n  \u003c!-- A.S.E 官网 --\u003e\n  \u003ca href=\"https://aicgseceval.tencent.com/home\"\u003e\n    \u003cimg src=\"https://img.shields.io/badge/🌐-A.S.E Website-blue?style=flat\u0026logo=\u0026logoColor=white\" alt=\"访问官网\"\u003e\n  \u003c/a\u003e\n  \u003c!-- 评测结果 --\u003e\n  \u003ca href=\"https://aicgseceval.tencent.com/rank\"\u003e\n    \u003cimg src=\"https://img.shields.io/badge/📊-Evaluation Results-success?style=flat\u0026logo=tencent\u0026logoColor=white\" alt=\"评测结果\"\u003e\n  \u003c/a\u003e\n  \u003c!-- 最新动态 --\u003e\n  \u003ca href=\"https://aicgseceval.tencent.com/updates\"\u003e\n    \u003cimg src=\"https://img.shields.io/badge/📰-A.S.E News \u0026 Updates-orange?style=flat\u0026logo=\u0026logoColor=white\" alt=\"最新动态\"\u003e\n  \u003c/a\u003e\n  \u003ca href=\"https://arxiv.org/abs/2508.18106\" target=\"_blank\"\u003e\n    \u003cimg src=\"https://img.shields.io/badge/📄-Paper-red?style=flat-rounded\u0026logo=\u0026logoColor=white\" alt=\"学术论文\"\u003e\n  \u003c/a\u003e\n  \u003c!-- HuggingFace 数据集 --\u003e\n  \u003c!-- \u003ca href=\"https://huggingface.co/datasets/tencent/AICGSecEval\" target=\"_blank\"\u003e\n    \u003cimg src=\"https://img.shields.io/badge/🤗-数据集-yellow?style=flat-rounded\u0026logo=huggingface\u0026logoColor=black\" alt=\"Hugging Face 数据集\"\u003e --\u003e\n  \u003c!-- \u003c/a\u003e --\u003e\n\u003c/p\u003e\n\n\nWe are committed to building **A.S.E (AICGSecEval)** into an **open, reproducible, and continuously evolving community project**. You are welcome to contribute through **Star**, **Fork**, **Issue**, or **Pull Request** to help expand the dataset and improve the evaluation framework. Your attention and contributions will help **A.S.E** grow, advancing both **industrial adoption** and **academic research** in **AI-generated code security**.\n\n\n\u003cp align=\"center\"\u003e\n  \u003ca href=\"https://github.com/Tencent/AICGSecEval\"\u003e\n      \u003cimg src=\"https://img.shields.io/badge/⭐-Give A.S.E a Star-yellow?style=flat\u0026logo=github\" alt=\"点亮Star\"\u003e\n  \u003c/a\u003e\n\u003c/p\u003e\n\n\n\n## Table of Contents\n\n- [✨ A.S.E Framework Design](#-ase-framework-design)\n- [🧱 2.0 Major Upgrades](#-20-major-upgrades)\n- [🚀 Quick Start](#-quick-start)\n- [📖 Citation](#-citation)\n- [🤝 Contribution Guide](#-contribution-guide)\n- [🙏 Acknowledgements](#-acknowledgements)\n- [📱 Join the Community](#-join-the-community)\n- [📄 License](#-license)\n\n\n## ✨ A.S.E Framework Design\n\n\u003cp align=\"center\"\u003e\n \u003cimg src=\"./img/arch_en.svg\" style=\"display: block; margin-left: auto; margin-right: auto;\"\u003e\n\u003c/p\u003e\n\n## 🧱 2.0 Major Upgrades\n\n1️⃣ **Dataset Upgrade – Broader Coverage of Code Generation Vulnerability Scenarios**  \nIncludes key risks from the OWASP Top 10 and CWE Top 25, covering 29 CWE vulnerability types across major programming languages such as C/C++, PHP, Java, Python, and JavaScript.  \n\n2️⃣ **Evaluation Target Upgrade – Support for Agentic Programming Tools**  \nExpands evaluation dimensions to better reflect real-world AI programming scenarios.  \n\n3️⃣ **Code Evaluation Upgrade – Static and Dynamic Hybrid Assessment**  \nIntroduces a dynamic evaluation scheme based on test cases and vulnerability PoCs, forming a hybrid assessment framework that balances detection breadth and verification precision, significantly enhancing the scientific rigor and practical value of the evaluation process.\n\n\n## 🚀 Quick Start\n\n**System Requirements**\n| Memory | Disk Space | Python | Docker |\n|:------:|:-----------:|:-------:|:--------:|\n| Recommended ≥16GB | ≥100GB | ≥3.11 | ≥27 |\n\n**1. Install Python Dependencies**  \n```\npip install -r requirements.txt\n```\n\n**2. Run Evaluation with One Command**  \n```\n# Basic Usage\npython3 invoke.py [options...] {--llm | --agent} [llm_options... | agent_options...]\n\n# View all available options\npython3 invoke.py -h\n\n# Example: LLM Evaluation\npython3 invoke.py \\\n  --llm \\\n  --model_name gpt-4o-2024-11-20 \\\n  --base_url https://api.openai.com/v1/ \\\n  --api_key sk-xxxxxx \\\n  --batch_id v1.0 \\\n  --dataset_path ./data/data_v2.json \\\n  --output_dir ./outputs\n  --max_workers 1\n  --github_token xxxxx // If not provided, anonymous cloning will be used, which may be subject to clone rate limiting.\n\n# Example: Agent Evaluation\nWhen running Agent-based evaluations, note that different Agents may require distinct configurations (e.g., model parameters, credentials, or APIs).\nThe launcher automatically forwards all unrecognized arguments (i.e., those not listed in -h) to the corresponding Agent module for parsing, allowing flexible extension of Agent-specific parameters.\n\nFor example, to evaluate Claude Code, run:\n\npython3 invoke.py \\\n  --agent \\\n  --agent_name claude_code \\\n  --batch_id v1.0 \\\n  --dataset_path ./data/data_v2.json \\\n  --claude_api_url https://ai.nengyongai.cn \\\n  --claude_api_key sk-XXXXX \\\n  --claude_model claude-sonnet-4-20250514\n  --github_token xxxxx // If not provided, anonymous cloning will be used, which may be subject to clone rate limiting.\n\nThe --claude_XXX options are parsed and used directly by the Agent evaluation module.\n```\n\n**Notes**  \n1️⃣ A full evaluation may take a long time depending on your hardware. You can adjust --max_workers to increase concurrency and reduce total runtime.  \n2️⃣ The tool supports automatic checkpoint recovery — if execution is interrupted, simply rerun the command to resume from the last state.\n\n\n## 📖 Citation\n\nIf your research uses or references **A.S.E** or its evaluation results, please cite it as follows:\n```bibtex\n@misc{lian2025aserepositorylevelbenchmarkevaluating,\n      title={A.S.E: A Repository-Level Benchmark for Evaluating Security in AI-Generated Code}, \n      author={Keke Lian and Bin Wang and Lei Zhang and Libo Chen and Junjie Wang and Ziming Zhao and Yujiu Yang and Miaoqian Lin and Haotong Duan and Haoran Zhao and Shuang Liao and Mingda Guo and Jiazheng Quan and Yilu Zhong and Chenhao He and Zichuan Chen and Jie Wu and Haoling Li and Zhaoxuan Li and Jiongchi Yu and Hui Li and Dong Zhang},\n      year={2025},\n      eprint={2508.18106},\n      archivePrefix={arXiv},\n      primaryClass={cs.SE},\n      url={https://arxiv.org/abs/2508.18106}, \n}\n```\n\n## 🤝 Contribution Guide\n\n**A.S.E** aims to build an **open, reproducible, and continuously evolving ecosystem** for evaluating the security of AI-generated code.\nWe welcome developers and researchers from academia, industry, and the open-source community to collaborate and contribute to the project.\n\n### Contribution Areas\n\n* 🧠 **Dataset Contribution**：Expand real-world vulnerability samples, enrich SAST tools/rules, and provide code functionality test cases and vulnerability PoCs.\n* ⚙️ **Framework Optimization**：Improve code generation logic, evaluation metrics, and context extraction strategies; support Agent integration and code refactoring.\n* 💡 **Discussions \u0026 Suggestions**：Propose new ideas, co-develop evaluation strategies, or share best practices.\n\u003e 💬 Beyond the above, we welcome any form of participation and support, including contributing real-world use cases, providing feedback, improving documentation, or joining community discussions.\n\n### Reference Documents\n\n\u003e 📌 If you plan to contribute, please read the following guides first to understand the data format, submission process, and validation standards.\n* 📘 Dataset Contribution Guide\n  * [Static Dataset Contribute](./docs/static_dataset_contribute.md)\n  * [Dynamic Dataset Contribute](./docs/dynamic_dataset_contribute.md)\n* 📘 [Agent Integration Guide](./docs/agent_contribute.md)\n\n\n### Community Interaction\n\n* 💭 Report issues or suggestions: via [Issues](https://github.com/Tencent/AICGSecEval/issues)\n* 💡 Brainstorm and discuss: join [Discussions](https://github.com/Tencent/AICGSecEval/discussions)\n\nYour engagement and contributions will help A.S.E evolve faster, expand its coverage, and advance the open standardization of AI-generated code security evaluation.\n\n\n\u003cbr\u003e\n\u003cp align=\"center\"\u003e\n  \u003ca href=\"https://github.com/Tencent/AICGSecEval\"\u003e\n      \u003cimg src=\"https://img.shields.io/badge/⭐-Give A.S.E a Star-yellow?style=flat\u0026logo=github\" alt=\"点亮Star\"\u003e\n  \u003c/a\u003e\n\u003c/p\u003e\n\n\u003c!-- ### 加入排行榜\n如果您有兴趣将您的模型评测结果提交到我们的官网，请按照 [TencentAISec/experiments](https://github.com/TencentAISec/experiments/blob/main/README_zh.md) 中发布的指令操作。 --\u003e\n\n\n## 🙏 Acknowledgements\n\nA.S.E​ is collaboratively developed by Tencent Security Platform Department with the following academic partners:\n\n* ​Fudan University​ (System Software \u0026 Security Lab)\n* Peking University​ (Prof. Hui Li's Team)\n* ​Shanghai Jiao Tong University​ (Institute of Network and System Security)\n* Tsinghua University​ (Prof. Yujiu Yang's Team)\n* Zhejiang University​ (Asst. Prof. Ziming Zhao's Team)\n\nWe sincerely appreciate their invaluable contributions to this project.\n\n**🙌 Contributors**\n\u003c!-- readme: contributors -start --\u003e\n\u003ca href=\"https://github.com/LianKee\" target=\"_blank\" rel=\"noopener noreferrer\" title=\"LianKee\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/23692514?v=4\" alt=\"LianKee\" width=\"48\" height=\"48\" style=\"border-radius: 50%; margin: 0 8px 8px 0; object-fit: cover;\" /\u003e\u003c/a\u003e\u003ca href=\"https://github.com/mfmans\" target=\"_blank\" rel=\"noopener noreferrer\" title=\"mfmans\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/10611031?v=4\" alt=\"mfmans\" width=\"48\" height=\"48\" style=\"border-radius: 50%; margin: 0 8px 8px 0; object-fit: cover;\" /\u003e\u003c/a\u003e\u003ca href=\"https://github.com/b2eeze\" target=\"_blank\" rel=\"noopener noreferrer\" title=\"b2eeze\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/125120249?v=4\" alt=\"b2eeze\" width=\"48\" height=\"48\" style=\"border-radius: 50%; margin: 0 8px 8px 0; object-fit: cover;\" /\u003e\u003c/a\u003e\u003ca href=\"https://github.com/GioldDiorld\" target=\"_blank\" rel=\"noopener noreferrer\" title=\"GioldDiorld\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/104082587?v=4\" alt=\"GioldDiorld\" width=\"48\" height=\"48\" style=\"border-radius: 50%; margin: 0 8px 8px 0; object-fit: cover;\" /\u003e\u003c/a\u003e\u003ca href=\"https://github.com/Ch0ser\" target=\"_blank\" rel=\"noopener noreferrer\" title=\"Ch0ser\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/180445268?v=4\" alt=\"Ch0ser\" width=\"48\" height=\"48\" style=\"border-radius: 50%; margin: 0 8px 8px 0; object-fit: cover;\" /\u003e\u003c/a\u003e\u003ca href=\"https://github.com/fish98\" target=\"_blank\" rel=\"noopener noreferrer\" title=\"fish98\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/33076808?v=4\" alt=\"fish98\" width=\"48\" height=\"48\" style=\"border-radius: 50%; margin: 0 8px 8px 0; object-fit: cover;\" /\u003e\u003c/a\u003e\u003ca href=\"https://github.com/MoefulYe\" target=\"_blank\" rel=\"noopener noreferrer\" title=\"MoefulYe\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/87225865?v=4\" alt=\"MoefulYe\" width=\"48\" height=\"48\" style=\"border-radius: 50%; margin: 0 8px 8px 0; object-fit: cover;\" /\u003e\u003c/a\u003e\u003ca href=\"https://github.com/jzquan\" target=\"_blank\" rel=\"noopener noreferrer\" title=\"jzquan\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/221012647?v=4\" alt=\"jzquan\" width=\"48\" height=\"48\" style=\"border-radius: 50%; margin: 0 8px 8px 0; object-fit: cover;\" /\u003e\u003c/a\u003e\u003ca href=\"https://github.com/Cycloctane\" target=\"_blank\" rel=\"noopener noreferrer\" title=\"Cycloctane\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/111191986?v=4\" alt=\"Cycloctane\" width=\"48\" height=\"48\" style=\"border-radius: 50%; margin: 0 8px 8px 0; object-fit: cover;\" /\u003e\u003c/a\u003e\u003ca href=\"https://github.com/HRsGIT\" target=\"_blank\" rel=\"noopener noreferrer\" title=\"HRsGIT\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/88483325?v=4\" alt=\"HRsGIT\" width=\"48\" height=\"48\" style=\"border-radius: 50%; margin: 0 8px 8px 0; object-fit: cover;\" /\u003e\u003c/a\u003e\u003ca href=\"https://github.com/TheBinKing\" target=\"_blank\" rel=\"noopener noreferrer\" title=\"TheBinKing\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/49024688?v=4\" alt=\"TheBinKing\" width=\"48\" height=\"48\" style=\"border-radius: 50%; margin: 0 8px 8px 0; object-fit: cover;\" /\u003e\u003c/a\u003e\u003ca href=\"https://github.com/JieWu02\" target=\"_blank\" rel=\"noopener noreferrer\" title=\"JieWu02\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/83527625?v=4\" alt=\"JieWu02\" width=\"48\" height=\"48\" style=\"border-radius: 50%; margin: 0 8px 8px 0; object-fit: cover;\" /\u003e\u003c/a\u003e\u003ca href=\"https://github.com/YilZhong\" target=\"_blank\" rel=\"noopener noreferrer\" title=\"YilZhong\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/122341077?v=4\" alt=\"YilZhong\" width=\"48\" height=\"48\" style=\"border-radius: 50%; margin: 0 8px 8px 0; object-fit: cover;\" /\u003e\u003c/a\u003e\u003ca href=\"https://github.com/FHMTT\" target=\"_blank\" rel=\"noopener noreferrer\" title=\"FHMTT\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/148672177?v=4\" alt=\"FHMTT\" width=\"48\" height=\"48\" style=\"border-radius: 50%; margin: 0 8px 8px 0; object-fit: cover;\" /\u003e\u003c/a\u003e\u003ca href=\"https://github.com/a7ca3\" target=\"_blank\" rel=\"noopener noreferrer\" title=\"a7ca3\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/56082688?v=4\" alt=\"a7ca3\" width=\"48\" height=\"48\" style=\"border-radius: 50%; margin: 0 8px 8px 0; object-fit: cover;\" /\u003e\u003c/a\u003e\u003ca href=\"https://github.com/Lucian-code233\" target=\"_blank\" rel=\"noopener noreferrer\" title=\"Lucian-code233\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/75003618?v=4\" alt=\"Lucian-code233\" width=\"48\" height=\"48\" style=\"border-radius: 50%; margin: 0 8px 8px 0; object-fit: cover;\" /\u003e\u003c/a\u003e\u003ca href=\"https://github.com/fangming3562\" target=\"_blank\" rel=\"noopener noreferrer\" title=\"fangming3562\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/55878931?v=4\" alt=\"fangming3562\" width=\"48\" height=\"48\" style=\"border-radius: 50%; margin: 0 8px 8px 0; object-fit: cover;\" /\u003e\u003c/a\u003e\u003ca href=\"https://github.com/krrrlww\" target=\"_blank\" rel=\"noopener noreferrer\" title=\"krrrlww\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/151769904?v=4\" alt=\"krrrlww\" width=\"48\" height=\"48\" style=\"border-radius: 50%; margin: 0 8px 8px 0; object-fit: cover;\" /\u003e\u003c/a\u003e\u003ca href=\"https://github.com/yumkea\" target=\"_blank\" rel=\"noopener noreferrer\" title=\"yumkea\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/152074213?v=4\" alt=\"yumkea\" width=\"48\" height=\"48\" style=\"border-radius: 50%; margin: 0 8px 8px 0; object-fit: cover;\" /\u003e\u003c/a\u003e\n\u003c!-- readme: contributors -end --\u003e\n\n## 📱 Join the Community\n\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"./img/wechat.jpg\"\u003e\n\u003c/p\u003e\n\n\n### 🔗 Recommended Security Tools\nIf you are interested in AI infrastructure security, refer to [A.I.G (AI-Infra-Guard)](https://github.com/Tencent/AI-Infra-Guard), a comprehensive, intelligent, and easy-to-use AI Red Teaming platform developed by Tencent Zhuque Lab.\n\n\n## 📄 License\nThis project is open source under the Apache-2.0 License. For more details, please refer to the [License.txt](./License.txt) file.\n\n\n---\n\n[![Star History Chart](https://api.star-history.com/svg?repos=Tencent/AICGSecEval\u0026type=Date)](https://www.star-history.com/#Tencent/AICGSecEval\u0026Date)","funding_links":[],"categories":["[↑](#table-of-contents)Benchmarks \u003ca name=\"benchmarking\"\u003e\u003c/a\u003e","AI应用"],"sub_categories":["**Code Security**"],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FTencent%2FAICGSecEval","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FTencent%2FAICGSecEval","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FTencent%2FAICGSecEval/lists"}