https://github.com/osu-nlp-group/webguard
WebGuard: Building a Generalizable Guardrail for Web Agents
https://github.com/osu-nlp-group/webguard
Last synced: about 1 year ago
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WebGuard: Building a Generalizable Guardrail for Web Agents
- Host: GitHub
- URL: https://github.com/osu-nlp-group/webguard
- Owner: OSU-NLP-Group
- License: mit
- Created: 2025-07-17T00:17:29.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-07-24T12:25:45.000Z (about 1 year ago)
- Last Synced: 2025-07-24T16:57:29.027Z (about 1 year ago)
- Language: Python
- Homepage:
- Size: 12.3 MB
- Stars: 2
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# WebGuard
[](https://github.com/OSU-NLP-Group/WebGuard)
[](https://arxiv.org/abs/2507.14293)
[](https://opensource.org/licenses/MIT)
[](https://creativecommons.org/licenses/by-nc/4.0/)
**WebGuard** is a generalizable guardrail system for web agents that helps detect potentially harmful actions before they are executed. This repository contains the complete codebase, dataset, and annotation tools for the WebGuard research project.
## 🚀 Overview
Web agents are becoming increasingly capable of performing complex tasks on websites, but this capability comes with significant safety risks. WebGuard addresses this challenge by providing:
- **Large-Scale Dataset**: Large-scale annotated dataset for training and evaluation
- **Annotation Tools**: Purpose-built tools for efficient data annotation
- **Model for Proactive Safety Monitoring**: Detects potentially harmful actions before execution
## 📊 Dataset
### WebGuard Dataset
This dataset contains web safety annotations for browser interactions. Each entry represents an annotated action on a website with a risk level.
Fields:
```bash
url: The URL where the action was performed
description: Description of the action (may be null)
tagHead: HTML tag type of the target element
Screenshot: Google Drive link to screenshot view
Annotation: Review classification (SAFE/UNSAFE/LOW/HIGH)
website: Website name/category
```
## 🔧 Annotation Tool
We provide a specialized annotation tool built upon the WebOlympus Chrome Extension for efficient web safety annotation.
### Getting Started
For complete installation instructions, usage guidelines, and annotation examples, please refer to:
📖 **[Annotation Tool Guide](annotation_tool/annotation_tool_introduction.md)**
This comprehensive guide covers:
- Chrome extension installation steps
- Annotation interface walkthrough
- Safety labeling guidelines
- Example annotations and best practices
- Troubleshooting common issues
The tool source code is available at `annotation_tool/webguard_annotation_tool.zip`.
## 🛠️ Installation
1. **Clone the repository**
```bash
git clone https://github.com/OSU-NLP-Group/WebGuard.git
cd WebGuard
```
2. **Install dependencies of LLaMA-Factory**
```bash
git clone --depth 1 https://github.com/hiyouga/LLaMA-Factory.git
cd LLaMA-Factory
pip install -e ".[torch,metrics]" --no-build-isolation
```
## 🏋️ Model Training
### Data Preparation
Before training, convert the raw dataset to LLaMA-Factory format:
```bash
# Convert multimodal data
python data_processing/multimodal_generata_factory_input_data.py
```
### Training Commands
#### Text-Only Models
**3B Model (Qwen2.5)**
```bash
llamafactory-cli train configs/3b_monitor_qwen2_5_a11y_full_sft_eval.yaml
```
**7B Model (Qwen2.5)**
```bash
llamafactory-cli train configs/7b_monitor_qwen2_5_a11y_full_sft_eval.yaml
```
#### Multimodal Models
**3B Vision-Language Model**
```bash
llamafactory-cli train configs/3b_monitor_qwen2_5vl_full_sft_eval.yaml
```
**7B Vision-Language Model**
```bash
llamafactory-cli train configs/7b_monitor_qwen2_5vl_full_sft_eval.yaml
```
## ⚠️ Disclaimer
This code and dataset are released solely for research purposes, with the goal of making the web more accessible and safer through language technologies. The authors strongly discourage any potentially harmful use of the data or technology by any party.
## 📄 License
- **Code**: MIT License
- **Dataset**: Creative Commons Attribution-NonCommercial 4.0 International
See [LICENSE](LICENSE) for full details.
## 📖 Citation
If you find this work useful, please consider starring our repository and citing our papers:
```bibtex
@article{zheng2025webguard,
title={WebGuard: Building a Generalizable Guardrail for Web Agents},
author={Zheng, Boyuan and Liao, Zeyi and Salisbury, Scott and Liu, Zeyuan and Lin, Michael and Zheng, Qinyuan and Wang, Zifan and Deng, Xiang and Song, Dawn and Sun, Huan and others},
journal={arXiv preprint arXiv:2507.14293},
year={2025}
}
@inproceedings{zheng-etal-2024-webolympus,
title = "{W}eb{O}lympus: An Open Platform for Web Agents on Live Websites",
author = "Zheng, Boyuan and Gou, Boyu and Salisbury, Scott and Du, Zheng and Sun, Huan and Su, Yu",
editor = "Hernandez Farias, Delia Irazu and Hope, Tom and Li, Manling",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-demo.20",
pages = "187--197",
}
```