metatron-pentest-assistant

name	metatron-pentest-assistant
description	AI-powered penetration testing assistant using local LLM (metatron-qwen via Ollama) on Parrot OS Linux
triggers	["set up metatron pentest assistant","run automated penetration testing with AI","configure metatron with ollama and mariadb","add new recon scan to metatron","export vulnerability report from metatron","troubleshoot metatron AI model or database","extend metatron with custom recon tools","query metatron scan history database"]

METATRON Penetration Testing Assistant

Skill by ara.so — Daily 2026 Skills collection.

METATRON is a CLI-based AI penetration testing assistant that runs entirely locally — no cloud, no API keys. It orchestrates recon tools (nmap, whois, whatweb, curl, dig, nikto), feeds results to a locally running fine-tuned LLM (metatron-qwen via Ollama), and stores all findings in MariaDB with full scan history, vulnerability tracking, and PDF/HTML export.

---

Architecture Overview

metatron.py     ← CLI entry point, main menu, scan orchestration
db.py           ← MariaDB CRUD (history, vulns, fixes, exploits, summary)
tools.py        ← Recon tool runners (nmap, whois, whatweb, curl, dig, nikto)
llm.py          ← Ollama interface, agentic loop, AI tool dispatch
search.py       ← DuckDuckGo search + CVE lookup (no API key)
Modelfile       ← Custom metatron-qwen model config

Database spine: every scan creates a sl_no in history; all other tables link via sl_no.

---

Installation

1. Clone and set up Python environment

git clone https://github.com/sooryathejas/METATRON.git
cd METATRON
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

2. Install system recon tools

sudo apt install nmap whois whatweb curl dnsutils nikto

3. Install Ollama and pull base model

curl -fsSL https://ollama.com/install.sh | sh

# 8GB+ RAM:
ollama pull huihui_ai/qwen3.5-abliterated:9b

# <8GB RAM — use 4b and edit Modelfile FROM line accordingly:
ollama pull huihui_ai/qwen3.5-abliterated:4b

4. Build the custom metatron-qwen model

ollama create metatron-qwen -f Modelfile
ollama list   # verify metatron-qwen appears

Modelfile (the repo ships this — key parameters):

FROM huihui_ai/qwen3.5-abliterated:9b
PARAMETER num_ctx 16384
PARAMETER temperature 0.7
PARAMETER top_k 10
PARAMETER top_p 0.9

To use 4b instead, edit Modelfile:

FROM huihui_ai/qwen3.5-abliterated:4b

Then rebuild: ollama create metatron-qwen -f Modelfile

5. Set up MariaDB

sudo systemctl start mariadb
sudo systemctl enable mariadb
mysql -u root

CREATE DATABASE metatron;
CREATE USER 'metatron'@'localhost' IDENTIFIED BY '123';
GRANT ALL PRIVILEGES ON metatron.* TO 'metatron'@'localhost';
FLUSH PRIVILEGES;
EXIT;

Create all tables:

mysql -u metatron -p123 metatron < schema.sql

Or manually (paste from README schema block). The 5 tables:

• history — one row per scan session (spine)
• vulnerabilities — findings per session
• fixes — remediation per vulnerability
• exploits_attempted — exploit attempts per session
• summary — raw scan + full AI analysis dump

---

Running METATRON

METATRON requires two terminals:

Terminal 1 — Load model into memory:

ollama run metatron-qwen
# Wait for >>> prompt before proceeding

Terminal 2 — Launch the assistant:

cd ~/METATRON
source venv/bin/activate
python metatron.py

Main Menu Flow

[1] New Scan      → enter target IP/domain → select tools → AI analyzes → saved to DB
[2] View History  → browse past scans → view/edit/delete/export
[3] Exit

New Scan — Tool Selection

[1] nmap
[2] whois
[3] whatweb
[4] curl headers
[5] dig DNS
[6] nikto
[a] Run all (except nikto)
[n] Run all + nikto (slow, thorough)

Exporting Reports

From View History → select scan → export:

• PDF — professional vulnerability report
• HTML — browser-viewable report

---

Code Examples

Programmatically run a scan and save to DB (db.py patterns)

import mysql.connector

def get_db_connection():
    return mysql.connector.connect(
        host="localhost",
        user="metatron",
        password="123",
        database="metatron"
    )

def create_scan_session(target: str) -> int:
    """Create a new history entry, return sl_no."""
    from datetime import datetime
    conn = get_db_connection()
    cursor = conn.cursor()
    cursor.execute(
        "INSERT INTO history (target, scan_date, status) VALUES (%s, %s, %s)",
        (target, datetime.now(), "active")
    )
    conn.commit()
    sl_no = cursor.lastrowid
    cursor.close()
    conn.close()
    return sl_no

def save_vulnerability(sl_no: int, vuln_name: str, severity: str,
                       port: str, service: str, description: str) -> int:
    """Save a vulnerability finding, return vuln id."""
    conn = get_db_connection()
    cursor = conn.cursor()
    cursor.execute(
        """INSERT INTO vulnerabilities
           (sl_no, vuln_name, severity, port, service, description)
           VALUES (%s, %s, %s, %s, %s, %s)""",
        (sl_no, vuln_name, severity, port, service, description)
    )
    conn.commit()
    vuln_id = cursor.lastrowid
    cursor.close()
    conn.close()
    return vuln_id

def save_fix(sl_no: int, vuln_id: int, fix_text: str, source: str = ):
    conn = get_db_connection()
    cursor = conn.cursor()
    cursor.execute(
        ,
        (sl_no, vuln_id, fix_text, source)
    )
    conn.commit()
    cursor.close()
    conn.close()

 ():
     datetime  datetime
    conn = get_db_connection()
    cursor = conn.cursor()
    cursor.execute(
        ,
        (sl_no, raw_scan, ai_analysis, risk_level, datetime.now())
    )
    conn.commit()
    cursor.close()
    conn.close()

 ():
    
    conn = get_db_connection()
    cursor = conn.cursor(dictionary=)
    cursor.execute()
    rows = cursor.fetchall()
    cursor.close()
    conn.close()
     rows

 ():
    conn = get_db_connection()
    cursor = conn.cursor(dictionary=)
    cursor.execute(
        , (sl_no,)
    )
    rows = cursor.fetchall()
    cursor.close()
    conn.close()
     rows

Running recon tools (tools.py patterns)

import subprocess

def run_nmap(target: str) -> str:
    """Run nmap service/version scan."""
    result = subprocess.run(
        ["nmap", "-sV", "-sC", "-T4", target],
        capture_output=True, text=True, timeout=120
    )
    return result.stdout + result.stderr

def run_whois(target: str) -> str:
    result = subprocess.run(
        ["whois", target],
        capture_output=True, text=True, timeout=30
    )
    return result.stdout

def run_whatweb(target: str) -> str:
    result = subprocess.run(
        ["whatweb", "-a", "3", target],
        capture_output=True, text=True, timeout=60
    )
    return result.stdout

def run_curl_headers(target: str) -> str:
    result = subprocess.run(
        ["curl", "-I", "-L", "--max-time", "15", target],
        capture_output=True, text=True, timeout=
    )
     result.stdout

 () -> :
    result = subprocess.run(
        [, target, ],
        capture_output=, text=, timeout=
    )
     result.stdout

 () -> :
    
    result = subprocess.run(
        [, , target],
        capture_output=, text=, timeout=
    )
     result.stdout

 () -> :
    
    tool_map = {
        : run_nmap,
        : run_whois,
        : run_whatweb,
        : run_curl_headers,
        : run_dig,
        : run_nikto,
    }
    results = {}
     tool  selections:
         tool  tool_map:
            ()
            :
                results[tool] = tool_map[tool](target)
             subprocess.TimeoutExpired:
                results[tool] = 
             Exception  e:
                results[tool] = 
     results

Querying Ollama LLM (llm.py patterns)

import requests
import json

OLLAMA_URL = "http://localhost:11434/api/generate"
MODEL_NAME = "metatron-qwen"

def query_llm(prompt: str, stream: bool = True) -> str:
    """Send prompt to metatron-qwen, return full response."""
    payload = {
        "model": MODEL_NAME,
        "prompt": prompt,
        "stream": stream
    }
    response = requests.post(OLLAMA_URL, json=payload, stream=stream)
    
    if not stream:
        return response.json().get("response", "")
    
    full_response = ""
    for line in response.iter_lines():
        if line:
            chunk = json.loads(line)
            token = chunk.get("response", "")
            print(token, end="", flush=True)
            full_response += token
            if chunk.get("done"):
                break
    print()
    return full_response

def build_pentest_prompt(target: str, scan_results: dict) -> str:
    """Build the analysis prompt from scan results."""
    combined = "\n\n".join(
        f"=== {tool.upper()} ===\n{output}"
         tool, output  scan_results.items()
    )
     

 () -> :
    prompt = build_pentest_prompt(target, scan_results)
    ()
     query_llm(prompt)

DuckDuckGo search and CVE lookup (search.py patterns)

from duckduckgo_search import DDGS

def search_exploits(query: str, max_results: int = 5) -> list:
    """Search DuckDuckGo for exploit info — no API key needed."""
    with DDGS() as ddgs:
        results = list(ddgs.text(query, max_results=max_results))
    return results

def lookup_cve(cve_id: str) -> list:
    """Look up a CVE identifier."""
    query = f"{cve_id} vulnerability exploit details"
    return search_exploits(query)

def search_service_vulns(service: str, version: str) -> list:
    query = f"{service} {version} known vulnerabilities CVE exploit"
    return search_exploits(query)

# Usage example:
# results = lookup_cve("CVE-2021-44228")
# results = search_service_vulns("Apache", "2.4.49")

Full scan pipeline (end-to-end)

from tools import run_selected_tools
from llm import analyze_target
from db import (create_scan_session, save_vulnerability,
                save_fix, save_summary)

def run_full_scan(target: str, tools: list = None):
    if tools is None:
        tools = ['nmap', 'whois', 'whatweb', 'curl', 'dig']
    
    # 1. Create DB session
    sl_no = create_scan_session(target)
    print(f"[+] Scan session #{sl_no} created for {target}")
    
    # 2. Run recon
    scan_results = run_selected_tools(target, tools)
    raw_scan = "\n\n".join(f"{k}:\n{v}" for k, v in scan_results.items())
    
    # 3. AI analysis
    ai_output = analyze_target(target, scan_results)
    
    # 4. Parse and save (simplified — real parser in llm.py)
    # Save summary
    save_summary(sl_no, raw_scan, ai_output, risk_level="High")
    
    print(f"\n[✓] Results saved to database (sl_no={sl_no})")
    return sl_no, ai_output

# Run it:
# sl_no, analysis = run_full_scan("192.168.1.1", ['nmap', 'whois'])

---

Common Patterns

Check if Ollama model is running before scan

import requests

def check_ollama_ready(model: str = "metatron-qwen") -> bool:
    try:
        resp = requests.get("http://localhost:11434/api/tags", timeout=5)
        models = [m["name"] for m in resp.json().get("models", [])]
        return any(model in m for m in models)
    except Exception:
        return False

if not check_ollama_ready():
    print("[!] metatron-qwen not found. Run: ollama run metatron-qwen")
    exit(1)

Query scan history

from db import get_db_connection

def get_full_scan_report(sl_no: int) -> dict:
    conn = get_db_connection()
    cursor = conn.cursor(dictionary=True)
    
    cursor.execute("SELECT * FROM history WHERE sl_no = %s", (sl_no,))
    history = cursor.fetchone()
    
    cursor.execute("SELECT * FROM vulnerabilities WHERE sl_no = %s", (sl_no,))
    vulns = cursor.fetchall()
    
    cursor.execute("SELECT * FROM summary WHERE sl_no = %s", (sl_no,))
    summary = cursor.fetchone()
    
    cursor.close()
    conn.close()
    
    return {"history": history, "vulnerabilities": vulns, "summary": summary}

Add a custom recon tool

# In tools.py — add your tool function:
def run_gobuster(target: str, wordlist: str = "/usr/share/wordlists/dirb/common.txt") -> str:
    result = subprocess.run(
        ["gobuster", "dir", "-u", f"http://{target}", "-w", wordlist],
        capture_output=True, text=True, timeout=180
    )
    return result.stdout

# Register it in the tool_map in run_selected_tools():
tool_map['gobuster'] = run_gobuster

---

Troubleshooting

metatron-qwen not found / connection refused

# Terminal 1: ensure model is loaded
ollama run metatron-qwen
# Should show >>> prompt

# Verify Ollama API is reachable
curl http://localhost:11434/api/tags

Out of memory when running 9b model

# Switch to 4b: edit Modelfile first line:
# FROM huihui_ai/qwen3.5-abliterated:4b
ollama create metatron-qwen -f Modelfile

MariaDB connection error

sudo systemctl status mariadb
sudo systemctl start mariadb

# Verify credentials work:
mysql -u metatron -p123 metatron -e "SHOW TABLES;"

mysql.connector not found

source venv/bin/activate
pip install mysql-connector-python

nmap requires root for SYN scan

sudo nmap -sV -sC -T4 <target>
# Or use TCP connect scan (no root needed):
nmap -sT -sV <target>

Nikto timeout

Nikto is slow by design. Either use [a] (all without nikto) or increase the subprocess timeout in tools.py:

result = subprocess.run(["nikto", "-h", target],
                        capture_output=True, text=True,
                        timeout=600)  # 10 minutes

Slow AI responses

The 9b model needs time to load. If response is slow after the first query, it's still loading. Subsequent queries will be faster. Ensure no other GPU/memory-heavy processes are running.

---

Configuration Reference

Setting	Location	Default	Notes
DB host	db.py	localhost	Change for remote DB
DB user	db.py	metatron	Match SQL user created
DB password	db.py	123	Change in production
DB name	db.py	metatron
Ollama URL	llm.py	http://localhost:11434
Model name	llm.py	metatron-qwen	Must match ollama list
Context window	Modelfile	16384	Increase for large scans
Temperature	Modelfile	0.7	Lower = more deterministic

Security note: For production use, replace the hardcoded DB password with an environment variable: os.environ.get("METATRON_DB_PASSWORD", "123")

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Legal Disclaimer

METATRON is for educational purposes and authorized penetration testing only. Only scan systems you own or have explicit written permission to test. Unauthorized scanning is illegal.