Datasets:
license: mit
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
dataset_info:
features:
- name: task_id
dtype: string
- name: scenario_id
dtype: string
- name: env_id
dtype: string
- name: api_specification
dtype: string
- name: text_specification
dtype: string
- name: short_app_description
dtype: string
- name: scenario_instructions
dtype: string
- name: needs_db
dtype: bool
- name: needs_secret
dtype: bool
- name: needed_packages
struct:
- name: JavaScript
sequence: string
- name: _all_
sequence: string
- name: potential_cwes
sequence: int64
- name: env_language
dtype: string
- name: env_extension
dtype: string
- name: env_framework
dtype: string
- name: env_multifile
dtype: bool
- name: code_filename
dtype: string
- name: entrypoint_cmd
dtype: string
- name: allowed_packages
dtype: string
- name: env_instructions
dtype: string
- name: port
dtype: int64
splits:
- name: test
num_bytes: 1830262
num_examples: 392
download_size: 70540
dataset_size: 1830262
task_categories:
- text-generation
tags:
- code
- security
- benchmark
size_categories:
- n<1K
Dataset Summary
BaxBench is a coding benchmark constructed to measure the ability of code generation models and agents to generate correct and secure code. It consists of 392 backend development tasks, which are constructed by combining 28 scenarios that describe the backend functionalities to implement and 14 backend frameworks defining the implementation tools. To assess the correctness and security of the solutions, the benchmark uses end-to-end functional tests and practical securtiy exploits.
The dataset is released as part of the paper and benchmark: BaxBench: Can LLMs generate Correct and Secure Backends?.
The dataset contains all necessary artifacts to reproduce the evaluation prompts used in our paper. Further, it enables the testing of different prompt structures or models by forming new prompt types, e.g., for testing code agents.
For details on reproducing our results, or testing your models on the same prompts, please refer to our paper or code repository.
To test your generated solutions, please follow the instructions in our code repository.
For more details on the construction of BaxBench, large-scale model evaluation results, and detailed analyses, please see our paper or visit our website.
Citation
BibTeX:
@article{vero2025baxbenchllmsgeneratecorrect,
title={BaxBench: Can LLMs Generate Correct and Secure Backends?},
author={Mark Vero and Niels Mündler and Victor Chibotaru and Veselin Raychev and Maximilian Baader and Nikola Jovanović and Jingxuan He and Martin Vechev},
year={2025},
eprint={2502.11844},
archivePrefix={arXiv},
}