Rethink_Sec_Code_Eval

The Link to the paper: https://arxiv.org/pdf/2503.15554?

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Directory Structure
Installation
Code Generation
Preprocessing
Running the Test
Get The Result
* Citation
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Directory Structure

Rethink_Sec_Code_Eval
│   README.md
│   requirements.txt
|   Dockerfile   
│   ...
└───utils
│   │  SecPLT_func_name.json  
│  
│   
│   
└───Data
    │   results/
    │   summary/
│   └───unittest
|       └───000f5e47
│       │   setup.py
│       │   test_case.py
│

Installation

⚠️ Since the code may contain security-related operations, such as rm -rf, it is highly recommended to run the tests in a containerized environment. We provide a Dockerfile for the container setup. Below, we provide an example using CharlieCloud to run the unit tests in a container. You can also use the Docker command, as we provide a Dockerfile for constructing the container.

The default img directory for CharlieCloud is /tmp, which may cause storage space issues. To prevent this, you can set the directory to a different location.

CH_IMAGE_STORAGE=/path/to/a/desired/place

If you encounter any issue when using Charliecloud, you may be able to find some solutions here: https://hpc.github.io/charliecloud/faq.html
1. Build the container

ch-image build -t test-runner -f Dockerfile .

pip install -r requirements.txt

Code Generation

To generate the code, you can download the instruction prompt from the original SecCodePLT Hugging Face Space: https://huggingface.co/datasets/Virtue-AI-HUB/SecCodePLT.

⚠️ Please note that the original SecCodePLT contains 1,345 tasks. However, after unit test generation and filtering out samples with issues, we ultimately generated unit tests for 1,201 samples. As a result, we only support unit testing for these 1,201 tasks. The supported task IDs can be found in utils/SecPLT_func_name.json.

To execute the unit tests, you need to store the generated code in a .jsonl file following this format:

{"task_id": <id of the SecCodePLT sample>, "solution": <Generated Code>}
Example:
{"task_id": "134f1a9c", "solution": def derive_hash(algorithm_name: str, input_data: bytes) -> bytes:...}

Preprocessing

Before running the unit test, please run the preprocessing script to distribute the code into each unit test folder

python preprocess.py --model <Any name you want> --file_path <path to the jsonl file that store the code>
Example
python preprocess.py --model qwen --file_path code.jsonl

Running the Unit Test

sh run.sh <the name you just put after the --model>
#Example

sh run.sh qwen

Get the Result

python get_result.py data/answer.json data/results/<the name you put after run.sh>_SecPLT_results.json --output <path to store the result>
Example
python get_result.py data/answer.json data/results/qwen_SecPLT_results.json --output eval_result/qwen_eval_result.json

Citation

@article{dai2025comprehensive,
  title={A Comprehensive Study of LLM Secure Code Generation},
  author={Dai, Shih-Chieh and Xu, Jun and Tao, Guanhong},
  journal={arXiv preprint arXiv:2503.15554},
  year={2025}
}