GuardBench

🔥 News

• [October 9, 2025] GuardBench now supports four additional datasets: JBB Behaviors, NicheHazardQA, HarmEval, and TechHazardQA. Also, it now allows for choosing the metrics to show at the end of the evaluation. Supported metrics are: precision (Precision), recall (Recall), f1 (F1), mcc (Matthews Correlation Coefficient), auprc (AUPRC), sensitivity (Sensitivity), specificity (Specificity), g_mean (G-Mean), fpr (False Positive Rate), fnr (False Negative Rate).

⚡️ Introduction

GuardBench is a Python library for the evaluation of guardrail models, i.e., LLMs fine-tuned to detect unsafe content in human-AI interactions. GuardBench provides a common interface to 40 evaluation datasets, which are downloaded and converted into a standardized format for improved usability. It also allows to quickly compare results and export LaTeX tables for scientific publications. GuardBench's benchmarking pipeline can also be leveraged on custom datasets.

GuardBench was featured in EMNLP 2024. The related paper is available here.

GuardBench has a public leaderboard available on HuggingFace.

You can find the list of supported datasets here. A few of them requires authorization. Please, read this.

If you use GuardBench to evaluate guardrail models for your scientific publications, please consider citing our work.

✨ Features

• 40 datasets for guardrail models evaluation.
• Automated evaluation pipeline.
• User-friendly.
• Extendable.
• Reproducible and sharable evaluation.
• Exportable evaluation reports.

🔌 Requirements

python>=3.10

💾 Installation

pip install guardbench

💡 Usage

from guardbench import benchmark

def moderate(
    conversations: list[list[dict[str, str]]],  # MANDATORY!
    # additional `kwargs` as needed
) -> list[float]:
    # do moderation
    # return list of floats (unsafe probabilities)

benchmark(
    moderate=moderate,  # User-defined moderation function
    model_name="My Guardrail Model",
    batch_size=1,              # Default value
    datasets="all",            # Default value
    metrics=["f1", "recall"],  # Default value
    # Note: you can pass additional `kwargs` for `moderate`
)

📖 Examples

• Follow our tutorial on benchmarking Llama Guard with GuardBench.
• More examples are available in the scripts folder.

📚 Documentation

Browse the documentation for more details about:

• The datasets and how to obtain them.
• The data format used by GuardBench.
• How to use the Report class to compare models and export results as LaTeX tables.
• How to leverage GuardBench's benchmarking pipeline on custom datasets.

🏆 Leaderboard

You can find GuardBench's leaderboard here. If you want to submit your results, please contact us.

👨‍💻 Authors

• Elias Bassani (European Commission - Joint Research Centre)

🎓 Citation

@inproceedings{guardbench,
    title = "{G}uard{B}ench: A Large-Scale Benchmark for Guardrail Models",
    author = "Bassani, Elias  and
      Sanchez, Ignacio",
    editor = "Al-Onaizan, Yaser  and
      Bansal, Mohit  and
      Chen, Yun-Nung",
    booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.emnlp-main.1022",
    doi = "10.18653/v1/2024.emnlp-main.1022",
    pages = "18393--18409",
}

🎁 Feature Requests

Would you like to see other features implemented? Please, open a feature request.

📄 License

GuardBench is provided as open-source software licensed under EUPL v1.2.