Text Classification
Transformers
Safetensors
English
distilbert
cybersecurity
xss
security
web
payload-detection
web-security
Instructions to use kd7979148/XSS_Payload_Detector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kd7979148/XSS_Payload_Detector with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kd7979148/XSS_Payload_Detector")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kd7979148/XSS_Payload_Detector") model = AutoModelForSequenceClassification.from_pretrained("kd7979148/XSS_Payload_Detector") - Notebooks
- Google Colab
- Kaggle
| <html lang="ko"> | |
| <head> | |
| <meta charset="utf-8"> | |
| <title>MINSUNG's XSS TEST PAGE</title> | |
| <link | |
| rel="stylesheet" | |
| href="{{ url_for('static', filename='style.css') }}" | |
| > | |
| </head> | |
| <body> | |
| <div class="hero"> | |
| <div class="overlay"> | |
| <h1>Great Memories~~</h1> | |
| <p> | |
| MINSUNG's xss test environment | |
| </p> | |
| </div> | |
| </div> | |
| <div class="container"> | |
| <h2>Input</h2> | |
| <form method="GET" action="/"> | |
| <input | |
| type="text" | |
| name="q" | |
| placeholder="Enter payload..." | |
| value="{{ q }}" | |
| > | |
| <button type="submit"> | |
| Submit | |
| </button> | |
| </form> | |
| <div class="result"> | |
| <h3>Reflected Output</h3> | |
| <div class="output"> | |
| {{ q|safe }} | |
| </div> | |
| </div> | |
| </div> | |
| <footer> | |
| </footer> | |
| </body> | |
| </html> |