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language:
- en
pipeline_tag: text-classification
library_name: transformers
tags:
- cybersecurity
- xss
- security
- web
- payload-detection
- text-classification
- web-security
---
# XSS Payload Detector
DistilBERT-based machine learning model for detecting XSS payloads.
This project can be used either as a standalone CLI classifier or as a log-monitoring system that automatically analyzes web server requests and detects potential XSS attacks.
## Labels
| Label | Description |
|---------|-------------|
| NORMAL | Benign input |
| XSS | Potential XSS payload |
## Requirements
```bash
pip install torch
pip install transformers
pip install flask
```
## Features
This project supports two different usage modes.
### 1. CLI Mode
Run:
```bash
python inference_bert_url.py
```
Enter a string or URL directly from the command line.
The model will classify the input as:
- NORMAL
- XSS
and display a confidence score.
Example:
```text
Input:
<script>alert(1)</script>
Result:
XSS
Confidence:
0.9998
```
---
### 2. Log Monitoring Mode
Run the monitoring service:
```bash
python monitor.py
```
The monitor automatically reads web server access logs and analyzes incoming requests.
Detected XSS payloads are logged for further inspection.
This allows the model to be integrated into a web application environment without manually entering payloads.
---
### Test Environment
Run the example Flask server:
```bash
python test_server.py
```
Then open:
```text
http://127.0.0.1:8080/?q=abcde
```
Example XSS payload:
```text
http://127.0.0.1:8080/?q=<img src='x' onerror='alert("xss")'>
```
The request will be reflected by the test page and analyzed by the monitoring service.
### Components
- `inference_bert_url.py`
- Standalone CLI tool for testing XSS detection.
- `moniter.py`
- Log monitoring service that reads web server logs and analyzes incoming requests.
- `test_server.py`
- Flask-based demonstration server for testing reflected XSS scenarios.
- `templates/`
- HTML templates used by the Flask demonstration server.
- `static/`
- Static assets (images, CSS, etc.) used by the Flask demonstration server.
## Model Files
- config.json
- model.safetensors
- tokenizer.json
- tokenizer_config.json
- vocab.txt
## Download Repository
To download the entire repository, including the trained model, example server, monitoring utility, templates, and static files:
```bash
pip install huggingface_hub
```
```python
from huggingface_hub import snapshot_download
snapshot_download(
repo_id="kd7979148/XSS_Payload_Detector"
)
```
This will download all files contained in the repository.
## Framework
- PyTorch
- Transformers |