khasinski/ai-bouncer
By khasinski · ↓ 6 · ♥ 1 · Updated 2026-02-05
What's in this repo
This page lists the files in khasinski/ai-bouncer, including embedding_model.onnx.
- 1 ONNX models (.onnx files)
- 8 files, including 4 .json, 1 .md, 1 .onnx, 1 .bin
- Last updated 2026-02-05
ONNX files
| File | Size | Download |
|---|---|---|
| embedding_model.onnx | — | Download |
How to use
These ONNX files can be loaded with ONNX Runtime. To inspect a model graph, upload the file to the Netron viewer. Download links point to the original files on Hugging Face.
README
AiBouncer - HTTP Request Attack Classifier
A lightweight ML model for detecting malicious HTTP requests in web applications. Built for the ai_bouncer Ruby gem.
Model Details
- Architecture: Model2Vec (distilled from MiniLM)
- Format: ONNX for cross-platform inference
- Size: ~33MB total
- Inference: ~2ms per request
- Embedding Dimension: 256
Attack Types Detected
| Label | Description | Severity |
|---|---|---|
sqli |
SQL Injection | High |
xss |
Cross-Site Scripting | High |
path_traversal |
Directory Traversal | High |
command_injection |
OS Command Injection | Critical |
credential_stuffing |
Automated Login Attempts | High |
spam_bot |
Comment/Form Spam | Medium |
scanner |
Vulnerability Scanning | Medium |
clean |
Legitimate Request | - |
Files
| File | Size | Description |
|---|---|---|
embedding_model.onnx |
29 MB | Model2Vec ONNX model |
vocab.json |
552 KB | Tokenizer vocabulary (29k tokens) |
tokenizer_config.json |
195 B | Tokenizer settings |
config.json |
99 B | Model configuration |
vectors.bin |
3 MB | Pre-computed attack pattern embeddings |
labels.json |
94 KB | Labels and metadata for vectors |
Usage with Ruby
# Gemfile
gem 'ai_bouncer'
# config/initializers/ai_bouncer.rb
AiBouncer.configure do |config|
config.enabled = true
config.auto_download = true # Downloads from HuggingFace automatically
end
Training Data
The model's vector database was built from:
- SecLists/fuzzdb - Security payload collections
- CSIC 2010 - HTTP attack dataset
- ModSecurity CRS - Rule patterns
- Real nginx logs - Credential stuffing, spam bots, scanners
Total: 3,053 labeled attack patterns
Performance
| Metric | Value |
|---|---|
| Accuracy | 92%+ |
| Mean Latency | 2ms |
| P99 Latency | 3ms |
| Memory Usage | ~30MB |
Contributing Training Data
This model uses a relatively small dataset (~3,000 patterns). Help make it better!
If you have access to real-world traffic data:
- Attack logs (WAF blocks, failed logins, spam)
- Clean traffic (normal API requests)
- False positives (incorrectly flagged requests)
Please consider contributing anonymized samples. Open an issue at github.com/khasinski/ai_bouncer.
License
MIT License
Citation
@software{ai_bouncer,
author = {Hasinski, Chris},
title = {AiBouncer: ML-powered HTTP Attack Detection for Rails},
url = {https://github.com/khasinski/ai_bouncer},
year = {2025}
}
Model info
Created: 2026-01-17
Updated: 2026-02-05
View on Hugging Face