AICGSecEval 是 AI Skill Hub 本期精选Agent工作流之一。综合评分 7.5 分，整体质量较高。我们推荐使用将其纳入你的 AI 工具库，帮助提升工作效率。
AICGSecEval是开源AI工作流：A.S.E，用于代码安全评估的仓库级别AI生成代码安全评估工具，帮助开发者快速评估代码安全性。
AICGSecEval 是一套完整的 AI Agent 自动化工作流方案。通过可视化的节点编排，将复杂的多步骤任务拆解为清晰的自动化流程，实现全程无人值守的智能处理。支持与数百种外部服务和 API 无缝集成，适合构建数据处理管线、业务自动化和 AI 辅助决策系统。
AICGSecEval是开源AI工作流：A.S.E，用于代码安全评估的仓库级别AI生成代码安全评估工具，帮助开发者快速评估代码安全性。
AICGSecEval 是一套完整的 AI Agent 自动化工作流方案。通过可视化的节点编排，将复杂的多步骤任务拆解为清晰的自动化流程，实现全程无人值守的智能处理。支持与数百种外部服务和 API 无缝集成，适合构建数据处理管线、业务自动化和 AI 辅助决策系统。
# 方式一：pip 安装（推荐）
pip install aicgseceval
# 方式二：虚拟环境安装（推荐生产环境）
python -m venv .venv
source .venv/bin/activate # Windows: .venv\Scripts\activate
pip install aicgseceval
# 方式三：从源码安装（获取最新功能）
git clone https://github.com/Tencent/AICGSecEval
cd AICGSecEval
pip install -e .
# 验证安装
python -c "import aicgseceval; print('安装成功')"
# 命令行使用
aicgseceval --help
# 基本用法
aicgseceval input_file -o output_file
# Python 代码中调用
import aicgseceval
# 示例
result = aicgseceval.process("input")
print(result)
# aicgseceval 配置文件示例（config.yml） app: name: "aicgseceval" debug: false log_level: "INFO" # 运行时指定配置文件 aicgseceval --config config.yml # 或通过环境变量配置 export AICGSECEVAL_API_KEY="your-key" export AICGSECEVAL_OUTPUT_DIR="./output"
<p align="center"> <h1 align="center"><img vertical-align=“middle” width="400px" src="img/title_header.png" alt="A.S.E"/></h1> </p>
<p align="center"> <a href="https://github.com/Tencent/AICGSecEval"> <img alt="Release" src="https://img.shields.io/github/v/release/Tencent/AICGSecEval?color=green"> </a> <a href="https://github.com/Tencent/AICGSecEval"> <img alt="GitHub Stars" src="https://img.shields.io/github/stars/Tencent/AICGSecEval?color=gold"> </a> <a href="https://github.com/Tencent/AICGSecEval"> <img alt="GitHub Stars" src="https://img.shields.io/github/forks/Tencent/AICGSecEval?color=gold"> </a> </p>
<br> <p align="center"> <h3 align="center">🚀 Repository-level AI-generated Code Security Evaluation Framework by <br>「Tencent Wukong Code Security Team」</h3> </p>
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: 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.
<p align="center"> <a href="https://aicgseceval.tencent.com/home"> <img src="https://img.shields.io/badge/🌐-A.S.E Website-blue?style=flat&logo=&logoColor=white" alt="访问官网"> </a> <a href="https://aicgseceval.tencent.com/rank"> <img src="https://img.shields.io/badge/📊-Evaluation Results-success?style=flat&logo=tencent&logoColor=white" alt="评测结果"> </a> <a href="https://aicgseceval.tencent.com/updates"> <img src="https://img.shields.io/badge/📰-A.S.E News & Updates-orange?style=flat&logo=&logoColor=white" alt="最新动态"> </a> <a href="https://arxiv.org/abs/2508.18106" target="_blank"> <img src="https://img.shields.io/badge/📄-Paper-red?style=flat-rounded&logo=&logoColor=white" alt="学术论文"> </a> </p>
We 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.
<p align="center"> <a href="https://github.com/Tencent/AICGSecEval"> <img src="https://img.shields.io/badge/⭐-Give A.S.E a Star-yellow?style=flat&logo=github" alt="点亮Star"> </a> </p>
System Requirements <div class="rdm-tbl-wrap"><table class="rdm-tbl"><thead><tr><th>Memory</th><th>Disk Space</th><th>Python</th><th>Docker</th></tr></thead><tbody><tr><td>Recommended ≥16GB</td><td>≥100GB</td><td>≥3.11</td><td>≥27</td></tr></tbody></table></div>
1. Install Python Dependencies
pip install -r requirements.txt2. Run Evaluation with One Command ```
python3 invoke.py [options...] {--llm | --agent} [llm_options... | agent_options...]
python3 invoke.py \ --llm \ --model_name gpt-4o-2024-11-20 \ --base_url https://api.openai.com/v1/ \ --api_key sk-xxxxxx \ --batch_id v1.0 \ --dataset_path ./data/data_v2.json \ --output_dir ./outputs --max_workers 1 --github_token xxxxx // If not provided, anonymous cloning will be used, which may be subject to clone rate limiting.
When running Agent-based evaluations, note that different Agents may require distinct configurations (e.g., model parameters, credentials, or APIs). The 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.
For example, to evaluate Claude Code, run:
python3 invoke.py \ --agent \ --agent_name claude_code \ --batch_id v1.0 \ --dataset_path ./data/data_v2.json \ --claude_api_url https://ai.nengyongai.cn \ --claude_api_key sk-XXXXX \ --claude_model claude-sonnet-4-20250514 --github_token xxxxx // If not provided, anonymous cloning will be used, which may be subject to clone rate limiting.
The --claude_XXX options are parsed and used directly by the Agent evaluation module. ```
Notes 1️⃣ A full evaluation may take a long time depending on your hardware. You can adjust --max_workers to increase concurrency and reduce total runtime. 2️⃣ The tool supports automatic checkpoint recovery — if execution is interrupted, simply rerun the command to resume from the last state.
A.S.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.
python3 invoke.py -h
📌 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
AICGSecEval是一个开源的AI工作流，用于代码安全评估，具有很好的开发体验和高效的评估能力，值得推荐。
该工具使用 NOASSERTION 协议，商用场景请仔细阅读协议条款，必要时咨询法律意见。
AI Skill Hub 为第三方内容聚合平台，本页面信息基于公开数据整理，不对工具功能和质量作任何法律背书。
建议在沙箱或测试环境中充分验证后，再部署至生产环境，并做好必要的安全评估。
📄 NOASSERTION — 请查阅原始协议条款了解具体使用限制。
经综合评估，AICGSecEval 在Agent工作流赛道中表现稳健，质量良好。如果你已有明确的使用需求，可以直接上手体验；如果还在评估阶段，建议对比同类工具后再做决策。
| 原始名称 | AICGSecEval |
| Topics | workflowagentaigcbenchmarkcodesecurityllmpython |
| GitHub | https://github.com/Tencent/AICGSecEval |
| License | NOASSERTION |
| 语言 | Python |
收录时间：2026-05-25 · 更新时间：2026-05-25 · License：NOASSERTION · AI Skill Hub 不对第三方内容的准确性作法律背书。
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