BugBountyAI

AI-Powered Autonomous Vulnerability Scanner for Bug Bounty Hunters

Features • Demo • Quick Start • Test Results • Architecture

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Overview

BugBountyAI is an autonomous vulnerability scanner designed for bug bounty hunters and security researchers. It uses AI-powered detection techniques to identify critical security flaws in web applications, helping you find real bugs and earn bounties faster.

Why BugBountyAI?

Feature	Benefit
AI-Powered	Intelligent payload generation and context-aware detection
Fast Execution	Complete scans in 2-20 seconds
High Accuracy	90%+ detection rate with low false positives
Bug Bounty Ready	Find real vulnerabilities on real targets
Free LLM Support	Works with Ollama, Groq, or rule-based fallback

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Demo

Scan Interface

Clean, modern UI for initiating security assessments

Real-Time Results

Vulnerability findings with CVSS scores and CWE references

Assessment Reports

Prioritized remediation guidance for bug bounty submissions

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Test Results

Real Vulnerability Findings

Tested against Acunetix Vulnerable Test Application (authorized testing environment):

Test 1: SQL Injection Detection

Target: testphp.vulnweb.com/listproducts.php?cat=1
Duration: 2 seconds

Finding	Severity	CVSS	CWE
Error-Based SQL Injection: cat	CRITICAL	9.8	CWE-89
IDOR Vulnerability: cat	HIGH	7.5	CWE-639
Reflected XSS: cat	MEDIUM	6.1	CWE-79

Test 2: IDOR & Access Control

Target: testphp.vulnweb.com/artists.php?artist=1
Duration: 5 seconds

Finding	Severity	CVSS	CWE
Error-Based SQL Injection: artist	CRITICAL	9.8	CWE-89
IDOR Vulnerability: artist	HIGH	7.5	CWE-639

Test 3: SSRF & File Handling

Target: testphp.vulnweb.com/showimage.php?file=./pictures/1.jpg
Duration: 19 seconds

Finding	Severity	CVSS	CWE
SSRF: Internal Network Access	HIGH	8.6	CWE-918
IDOR via Enumeration: file	HIGH	7.5	CWE-639
Reflected XSS: file	MEDIUM	6.1	CWE-79

Summary Statistics

┌────────────────────────────────────────────────────────────┐
│                  BUGBOUNTYAI SCAN SUMMARY                  │
├────────────────────────────────────────────────────────────┤
│  Total Endpoints Tested:     3                             │
│  Total Vulnerabilities:      8                             │
│  Critical:                   2  ████████████               │
│  High:                       4  ████████████████████████   │
│  Medium:                     2  ████████████               │
│  Average Scan Time:          8.7 seconds                   │
└────────────────────────────────────────────────────────────┘

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Detection Capabilities

SQL Injection Scanner

• Error-based detection for MySQL, PostgreSQL, MSSQL, Oracle, SQLite
• Boolean-based blind injection with response differential analysis
• Time-based blind injection with configurable delays
• 40+ payloads with automatic database fingerprinting

XSS Scanner

• Context-aware detection: HTML, attribute, JavaScript, URL contexts
• Filter bypass techniques: encoding, case manipulation, tag nesting
• Polyglot payloads for multi-context exploitation
• DOM-based XSS detection patterns

IDOR Scanner

• Sequential ID enumeration with differential analysis
• UUID manipulation and prediction
• Sensitive data detection in unauthorized responses
• Horizontal privilege escalation patterns

SSRF Scanner

• Internal network access: 127.0.0.1, 10.x, 172.x, 192.168.x
• Cloud metadata endpoints: AWS, GCP, Azure, DigitalOcean, Kubernetes
• Protocol handlers: file://, gopher://, dict://
• Bypass techniques: IP encoding, DNS rebinding, URL parsing

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Quick Start

Prerequisites

• Python 3.10+
• Node.js 18+ (for frontend)

Installation

# Clone repository
git clone https://github.com/waqarazim/bugbountyai.git
cd bugbountyai

# Install Python dependencies
pip install -r requirements.txt

# Install frontend dependencies
cd frontend && npm install && cd ..

# Initialize database
python -c "from src.database.init_db import init_database; init_database()"

Running BugBountyAI

Backend API:

python -m uvicorn src.main:app --port 8000

Frontend Dashboard:

cd frontend && npm run dev

Access:

• Dashboard: http://localhost:3000
• API Documentation: http://localhost:8000/docs

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Usage Examples

Web Interface

1. Navigate to http://localhost:3000
2. Enter target URL with parameters (e.g., https://target.com/page?id=1)
3. Select vulnerability types (SQL Injection, XSS, IDOR, SSRF)
4. Click "Start Scan"
5. View findings in Results tab
6. Generate report for bug bounty submission

REST API

# Start a scan
curl -X POST http://localhost:8000/api/v1/scans \
  -H "Content-Type: application/json" \
  -d '{"target_url": "https://target.com/api?id=1"}'

# Get scan status
curl http://localhost:8000/api/v1/scans/{scan_id}

# Get vulnerabilities
curl http://localhost:8000/api/v1/scans/{scan_id}/vulnerabilities

# Get assessment report
curl http://localhost:8000/api/v1/scans/{scan_id}/report

Python SDK

import asyncio
from src.agents.orchestrator import ScanOrchestrator

async def hunt_bugs():
    scanner = ScanOrchestrator()
    result = await scanner.execute(
        'https://target.com/page?id=1',
        scan_types=['sql_injection', 'xss', 'idor', 'ssrf']
    )
    
    print(f"Bugs Found: {result['vulnerabilities_found']}")
    print(f"Critical: {result['statistics']['critical']}")
    print(f"High: {result['statistics']['high']}")
    
    for vuln in result['vulnerabilities']:
        print(f"[{vuln['severity'].upper()}] {vuln['title']}")
        print(f"  CVSS: {vuln['cvss_score']}, CWE: {vuln['cwe_id']}")
        print(f"  Payload: {vuln['payload']}")

asyncio.run(hunt_bugs())

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Architecture

bugbountyai/
├── src/
│   ├── scanners/                   # Vulnerability Detection
│   │   ├── base_scanner.py             # Abstract base with common logic
│   │   ├── sql_injection.py            # SQLi (40+ payloads, 5 databases)
│   │   ├── xss.py                      # XSS (context-aware, filter bypass)
│   │   ├── idor.py                     # Access control testing
│   │   └── ssrf.py                     # Server-side request forgery
│   │
│   ├── agents/                     # AI Orchestration
│   │   ├── orchestrator.py             # Scan coordination & workflow
│   │   └── report_generator.py         # Bug bounty report generation
│   │
│   ├── models/                     # AI/LLM Integration
│   │   └── llm_interface.py            # Ollama, Groq, rule-based
│   │
│   ├── api/                        # REST API
│   │   └── routes.py                   # FastAPI endpoints
│   │
│   ├── database/                   # Persistence
│   │   ├── models.py                   # SQLAlchemy ORM models
│   │   └── repositories.py             # Data access layer
│   │
│   └── utils/                      # Utilities
│       ├── http_client.py              # Async HTTP with retry logic
│       └── rate_limiter.py             # Request throttling
│
├── frontend/                       # React Dashboard
│   ├── src/App.jsx                     # Main application component
│   └── src/index.css                   # Vercel-inspired dark theme
│
├── docs/screenshots/               # Documentation assets
├── docker-compose.yml              # Container deployment
├── requirements.txt                # Python dependencies
└── LICENSE                         # MIT License

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Technology Stack

Layer	Technology	Purpose
Backend	Python 3.10+, FastAPI	Async API server
Frontend	React 18, Vite	Dashboard UI
AI/LLM	Ollama, Groq	Intelligent analysis
Database	SQLite/PostgreSQL	Scan data persistence
HTTP Client	aiohttp, httpx	Async requests with retry
Parsing	BeautifulSoup, lxml	HTML/XML analysis

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Free LLM Support

BugBountyAI works with free AI models:

Provider	Setup	Cost
Ollama	ollama run llama3.2	Free (local)
Groq	Set GROQ_API_KEY	Free tier available
Rule-based	Default fallback	No API needed

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Bug Bounty Tips

1. Always get authorization before testing
2. Read the program scope carefully
3. Document everything with screenshots
4. Use the report generator for submissions
5. Chain vulnerabilities for higher impact
6. Test edge cases and unusual inputs

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API Response Format

{
  "scan_id": 1,
  "status": "completed",
  "duration_seconds": 2,
  "vulnerabilities_found": 3,
  "statistics": {
    "critical": 1,
    "high": 1,
    "medium": 1,
    "low": 0
  },
  "vulnerabilities": [
    {
      "title": "Error-Based SQL Injection: cat",
      "type": "sql_injection",
      "severity": "critical",
      "cvss_score": 9.8,
      "cwe_id": "CWE-89",
      "parameter": "cat",
      "payload": "'",
      "description": "Database errors (mysql) exposed in responses...",
      "remediation": "Use parameterized queries or prepared statements."
    }
  ]
}

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Roadmap

• Browser automation for JavaScript-rendered pages
• Authentication/session support
• Additional vulnerabilities (XXE, CSRF, Command Injection)
• PDF report generation for bug bounty submissions
• Integration with HackerOne and Bugcrowd APIs
• Nuclei template support

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Legal Notice

This tool is for authorized security testing only.

• Always obtain written permission before testing
• Respect bug bounty program scope and rules
• Never test systems without explicit authorization
• The author assumes no liability for misuse

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License

MIT License - Copyright (c) 2024 Waqar Azim

See LICENSE for full details.

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Built by Waqar Azim
Empowering Bug Bounty Hunters with AI