AICGSecEval

Tencent/AICGSecEval is ranked 15,296 among these 8 peers; moeru-ai/xsai is ranked 15,288.

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: **Code Generation Tasks** – Derived from real-world GitHub projects and authoritative CVE patches, ensuring both practical relevance and security sensitivity. **Code Generation Process** – Automatically extracts project-level code context to accurately simulate realistic AI programming scenarios. **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. 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. E** grow, advancing both **industrial adoption** and **academic research** in **AI-generated code security**. 0 Major Upgrades 🚀 Quick Start 📖 Citation 🤝 Contribution Guide 🙏 Acknowledgements

📱 Join the Community 📄 License ✨ A. 0 Major Upgrades 1️⃣ **Dataset Upgrade – Broader Coverage of Code Generation Vulnerability Scenarios** Includes 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. 2️⃣ **Evaluation Target Upgrade – Support for Agentic Programming Tools** Expands evaluation dimensions to better reflect real-world AI programming scenarios. 3️⃣ **Code Evaluation Upgrade – Static and Dynamic Hybrid Assessment** Introduces 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. Run Evaluation with One Command** **Notes** 1️⃣ A full evaluation may take a long time depending on your hardware. 📖 Citation If your research uses or references **A. E** or its evaluation results, please cite it as follows: 🤝 Contribution Guide **A. E** aims to build an **open, reproducible

, and continuously evolving ecosystem** for evaluating the security of AI-generated code. We welcome developers and researchers from academia, industry, and the open-source community to collaborate and contribute to the project. Contribution Areas 🧠 **Dataset Contribution**：Expand real-world vulnerability samples, enrich SAST tools/rules, and provide code functionality test cases and vulnerability PoCs. ⚙️ **Framework Optimization**：Improve code generation logic, evaluation metrics, and context extraction strategies; support Agent integration and code refactoring. 💡 **Discussions & Suggestions**：Propose new ideas, co-develop evaluation

strategies, or share best practices. 💬 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. Reference Documents 📌 If you plan to contribute, please read the following guides first to understand the data format, submission process, and validation standards. 📘 Dataset Contribution Guide Static Dataset Contribute Dynamic Dataset Contribute 📘 Agent Integration Guide Community Interaction 💭 Report issues or suggestions: via Issues 💡 Brainstorm and discuss: join Discussions Your engagement

and contributions will help A. E evolve faster, expand its coverage, and advance the open standardization of AI-generated code