enumeraite

KO | EN

gitlite — search

#javascript #python #llm #golang #go #android #rust #linux #java #awesome #cli #kubernetes #security #mcp

(★ 45)

AI-Powered Web Attack Surface Enumeration

enumeraite 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

<h1 align="center">enumeraite</h1>

AI-Powered Web Attack Surface Enumeration

Proof-of-concept research demonstrating the future of intelligent enumeration. 
 Traditional wordlists are dead. AI-driven discovery is the future.

<a href="https://enumeraite.com">🌐 Website</a> •
 <a href="https://huggingface.co/enumeraite">🤗 Models</a> •
 <a href="https://github.com/oz9un/enumeraite">📦 GitHub</a> •
 <a href="https://www.youtube.com/watch?v=IzsBS_E2RVY">📹 Talk</a>

---

Research presented at DEFCON 33 Recon Village 
 by <a href="https://github.com/oz9un">Özgün Kültekin</a> (<a href="https://x.com/oz9un">@oz9un</a>)

---

## 📺 Watch the Talk

Click to watch the presentation

---

## Features

### Two Modes of Operation

| Mode | Command | Purpose |
|------|---------|---------|
| **Generate** | `enumeraite generate` | Bulk generation from wordlists - feed it known paths/subdomains, get intelligent variants |
| **Analyze** | `enumeraite analyze` | Deep analysis of single targets - understand patterns and generate context-aware results |

### Capabilities

| Feature | Description |
|---------|-------------|
| **Path Generation** | Generate API endpoints from known paths using AI pattern recognition |
| **Subdomain Generation** | Generate subdomains based on naming patterns and conventions |
| **DNS Validation** | Validate generated subdomains via DNS resolution (`--validate`) |
| **HTTP Validation** | Check HTTP/HTTPS response for validated subdomains (`--check-http`) |
| **Pattern Analysis** | Deep decomposition of complex naming patterns (analyze mode) |
| **Function-based Discovery** | Find endpoints for specific functionality like "user deletion" or "admin ops" |
| **Debug Mode** | Track token usage and cost estimation (`--debug`) |
| **Tool Integration** | Pipe output directly to ffuf, gobuster, dirb, nuclei |

### Supported Providers

| Provider | Setup | Quality | Download Size | Included by Default |
|----------|-------|---------|---------------|-------------------|
| **Claude** | `export ANTHROPIC_API_KEY='...'` | ⭐⭐⭐⭐⭐ Excellent | ~3MB | ✅ Yes |
| **OpenAI** | `export OPENAI_API_KEY='...'` | ⭐⭐⭐⭐⭐ Excellent | ~5MB | ✅ Yes |
| **HuggingFace** | `pip install "enumeraite[huggingface]"` | ⭐⭐⚫⚫⚫ Limited | ~3GB | ❌ Optional |

## Quick Start

### 30-Second Start

```bash
# Install enumeraite (includes Claude + OpenAI support)
pip install enumeraite

# Set API key
export ANTHROPIC_API_KEY='your-key-here'

# Create sample paths file
echo -e "/api/users\n/api/auth/login\n/admin/dashboard" > paths.txt

# Generate intelligent variants
enumeraite generate path --input paths.txt --count 20
```

### Installation

**Default (Recommended) - Best Quality, Lightweight:**
```bash
# Includes Claude + OpenAI providers (~8MB download)
pip install enumeraite
```

**With Local Models (Heavy Download):**
```bash
# Adds HuggingFace models (~3GB+ download, limited quality)
pip install "enumeraite[huggingface]"

# Everything including heavy local models
pip install "enumeraite[all]"
```

**From Source (Development):**
```bash
git clone https://github.com/oz9un/enumeraite.git
cd enumeraite
pip install -e .
```

> **💡 Why this structure?**
>
> - **Default**: Get the best quality tools (Claude + OpenAI) with minimal download
> - **HuggingFace**: Only install if you need offline/local models (much larger, lower quality)

### Setup

Both Claude and OpenAI are included by default! Just add your API key:

**Claude (Recommended):**
```bash
# Get API key from https://console.anthropic.com/
export ANTHROPIC_API_KEY='your-api-key-here'

# Ready to use!
enumeraite generate path --input paths.txt --provider claude --count 20
```

**OpenAI:**
```bash
# Get API key from https://platform.openai.com/api-keys
export OPENAI_API_KEY='your-api-key-here'

# Ready to use!
enumeraite generate path --input paths.txt --provider openai --count 20
```

**HuggingFace Local Models (Optional):**
```bash
# First install the heavy dependencies (~3GB)
pip install "enumeraite[huggingface]"

# No API key needed, but quality is limited
enumeraite generate path --input paths.txt --provider huggingface --model enumeraite/Enumeraite-x-Qwen3-4B-Path --count 20
```

## 💡 Smart Dependency Design

**Why we include Claude + OpenAI by default:**

| Approach | Download Size | Quality | Cost | Offline |
|----------|---------------|---------|------|---------|
| **Default (Claude/OpenAI)** | ~8MB | ⭐⭐⭐⭐⭐ | Pay-per-use | ❌ |
| **HuggingFace Local** | ~3GB+ | ⭐⭐⚫⚫⚫ | Free | ✅ |

- **Most users want**: High quality results, minimal setup → **Use default**
- **Researchers/offline use**: Local models, no API costs → **Add `[huggingface]`**

### Basic Usage Examples

**Path Discovery:**
```bash
# Start with known endpoints
echo "/api/users
/api/auth/login
/admin/dashboard" > known_paths.txt

# Generate intelligent variants
enumeraite generate path --input known_paths.txt --provider claude --count 25
```

**Subdomain Discovery:**
```bash
# Known subdomains
echo "api.example.com
admin.example.com
staging.example.com" > known_subs.txt

# Generate with DNS validation
enumeraite generate subdomain --input known_subs.txt --provider claude --validate --count 30
```

### Understanding Token Usage and Models

```bash
# Monitor token usage with debug flag (Claude + OpenAI included by default)
enumeraite generate path --input paths.txt --provider claude --count 25 --debug

# Use specific models (no extra installation needed)
enumeraite generate path --input paths.txt --provider openai --model gpt-4 --count 20
enumeraite generate path --input paths.txt --provider claude --model anthropic/claude-sonnet-4 --count 20

# HuggingFace models (requires: pip install "enumeraite[huggingface]")
enumeraite generate path --input paths.txt --provider huggingface --model enumeraite/Enumeraite-x-Qwen3-4B-Subdomain --count 15
```

## Examples

### Bulk Path Generation

```bash
enumeraite generate path --input my_paths.txt --provider claude --count 20
```

### Subdomain Generation with DNS Validation

```bash
enumeraite generate subdomain --input my_subdomains.txt --provider claude --validate --count 30
```

### Path Function Analysis

```bash
enumeraite analyze path "/api/Usr_crt" --function "user deletion" --provider claude
```

### Subdomain Pattern Analysis

```bash
enumeraite analyze subdomain "activateiphone-use1-cx02.example.com" --provider claude
```

## Tool Integration

Enumeraite output is designed to work seamlessly with popular fuzzing tools.

### Pipe to ffuf

```bash
# Generate paths and fuzz directly
enumeraite generate path -i known_paths.txt -c 100 | ffuf -w - -u https://target.com/FUZZ

# Save to file first, then use
enumeraite generate path -i known_paths.txt -o wordlist.txt
ffuf -w wordlist.txt -u https://target.com/FUZZ
```

### Pipe to gobuster

```bash
enumeraite generate path -i known_paths.txt | gobuster dir -u https://target.com -w -
```

### With nuclei

```bash
# Generate subdomains, validate, then scan
enumeraite generate subdomain -i subs.txt --validate -o live_subs.txt
nuclei -l live_subs.txt -t cves/
```

## Command Reference

### generate path
```
enumeraite generate path -i <input> [options]

Options:
  -i, --input PATH     Input file with known paths (required)
  -o, --output PATH    Output file (default: stdout)
  -c, --count INT      Number to generate (default: 50)
  --provider TEXT      AI provider: claude, openai, huggingface
  --model TEXT         Specific model to use
  --debug              Show token usage and cost
```

### generate subdomain
```
enumeraite generate subdomain -i <input> [options]

Options:
  -i, --input PATH     Input file with known subdomains (required)
  -o, --output PATH    Output file (default: stdout)
  -c, --count INT      Number to generate (default: 50)
  --provider TEXT      AI provider: claude, openai, huggingface
  --model TEXT         Specific model to use
  --validate           Enable DNS validation
  --check-http         Check HTTP response (requires --validate)
  --debug              Show token usage and cost
```

### analyze path
```
enumeraite analyze path <path> -f <function> [options]

Options:
  -f, --function TEXT  Functionality to find (required)
  -c, --count INT      Number of variants (default: 20)
  -o, --output PATH    Output file (default: stdout)
  --provider TEXT      AI provider: claude, openai, huggingface
  --debug              Show debug info
```

### analyze subdomain
```
enumeraite analyze subdomain <subdomain> [options]

Options:
  -c, --count INT      Number of variants (default: 20)
  -o, --output PATH    Output file (default: stdout)
  --provider TEXT      AI provider: claude, openai, huggingface
  --debug              Show debug info
```

## Future Vision

This research opens several exciting directions:

### Near-term Improvements:
- **Better fine-tuned models** trained on real application data
- **Target-specific wordlist generation** based on technology stack
- **Response-aware fuzzing** that adapts based on HTTP responses
- **Integration with existing tools** (ffuf, dirb, gobuster)

### Long-term Potential:
- **RAG-enhanced models** with application-specific knowledge bases
- **Multi-modal analysis** incorporating HTML, JavaScript, and API schemas

### Research Applications:
- Academic study of AI in offensive security
- Benchmark for evaluating enumeration approaches
- Foundation for specialized security AI models

## Quality Comparison

| Model | Quality | Consistency | Cost | Use Case |
|-------|---------|-------------|------|----------|
| **Claude Sonnet** | ⭐⭐⭐⭐⭐ | Excellent | Low | Production research |
| **GPT-4** | ⭐⭐⭐⭐⭐ | Very Good | Medium | Production research |
| **Custom Enumeraite Models** | ⭐⭐⚫⚫⚫ | Poor | Free | Demo/testing only |

## Contributing to Research

We welcome contributions that advance the methodology:

- **Model improvements** and training data
- **Integration with existing tools**
- **Novel enumeration techniques**
- **Evaluation metrics and benchmarks**
- **Real-world case studies**

## License

This research project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

---

⚠️ Ethical Use Disclaimer 
 This research tool is intended for authorized security testing and academic research only. 
 Users are responsible for ensuring they have proper permission to test target systems. 
 
 Research Status 
 This is proof-of-concept research software. Results may vary. 
 For production security testing, combine with traditional methods.

---

"The future of enumeration is not about having the biggest wordlist, 
 but about having the smartest approach."

---

For detailed documentation, examples, and advanced usage: 
 <a href="https://enumeraite.com">Visit enumeraite.com</a>

enumeraite

enumeraite 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

enumeraite

enumeraite 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README