🧠⚡ SQLMAP SKYNET - Autonomous AI v1.2.0

AI-assisted SQLMap orchestration + real-time dashboard + MCP tool server (agent-ready)

A guided, repeatable workflow: Targets → Preflight → Phased Scan → AI Assist → Evidence → Report

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🔥 Update v1.2.0 — Autonomous AI + MCPFast Commands

✅ Renamed branding from Intelligence → Autonomous AI
✅ Added MCPFast (FastMCP) command-line usage (No UI required)
✅ MCP supports URL scan and targetlist (.txt) scan
✅ Best for Windows + Linux servers (headless / no GUI)

What is SQLMAP SKYNET (simple)

SQLMAP SKYNET wraps sqlmap.py with a Neural Ops workflow — fast, structured, and trackable.

✅ Operational phases (Detect → Bypass → Enumerate → Dump)
✅ Dashboard visibility (logs, stats, artifacts)
✅ Autonomous AI tuning (Ollama / Cloud)
✅ RAG intelligence (learns from success, recalls patterns)
✅ MCP tool server (agent automation)

Goal: higher signal, less noise, and reports real proof.

workflow : Dashboard + MCP + AI + RAG

flowchart TD
  A["1. Targets<br/>single URL or list"] --> B["2. Preflight<br/>GET or POST + headers + cookies + safe limits"]

  B --> C["3A. Dashboard Control<br/>FastAPI UI + WebSocket"]
  B --> D["3B. MCP Control<br/>Agent tool calls"]

  C --> E["4. Runner Core<br/>SQLMapRunner -> SQLMapBackend -> sqlmap.py"]
  D --> E

  E --> F["5A. Autonomous AI<br/>Ollama first, cloud fallback"]
  E --> G["5B. RAG Memory<br/>learn + recall"]
  E --> H["5C. Web Intel<br/>optional search"]
  E --> I["5D. WAF Intel<br/>fingerprint + bypass hints"]

  F --> J["6. Artifacts<br/>reports + logs + sessions"]
  G --> J
  H --> J
  I --> J

  J --> K["7. View or Export<br/>Dashboard + files"]

Neural Ops Stack (Simple)

flowchart TD
  subgraph INPUT["INPUT"]
    T["Targets"] --> PF["Preflight"]
  end

  subgraph CONTROL["CONTROL"]
    UI["Dashboard"] --- MCP["MCP Client"]
  end

  subgraph ENGINE["ENGINE"]
    RUN["Runner Core"] --> SM["sqlmap.py"]
  end

  subgraph INTEL["INTEL"]
    AI["Autonomous AI"] --- RAG["RAG Memory"] --- WEB["Web Intel"] --- WAF["WAF Intel"]
  end

  subgraph OUTPUT["OUTPUT"]
    ART["Artifacts"] --> REP["Reports"]
  end

  PF --> CONTROL
  CONTROL --> ENGINE
  ENGINE <--> INTEL
  ENGINE --> OUTPUT

Key idea: MCP and Dashboard are two ways to drive the same engine logic:

Dashboard = human operator UI
MCP = agent/tool interface

📸 Screenshots of Feature

🧾 Report Samples

🎥 Demo Video

Windows requirements (choose your AI mode)

✅ Minimum (no local AI)

Windows 10/11 64-bit
Python 3.12+
Git
sqlmap.py present (recommended: .\sqlmap\sqlmap.py)
Any CPU / 8GB RAM

Use Cloud AI (OpenAI / Groq / DeepSeek / Kimi / Claude) or run with AI features OFF.

✅ Recommended (local AI with Ollama)

Windows 10/11 64-bit
Python 3.12+
16GB RAM (recommended)
GPU optional (faster with Nvidia GPU + VRAM)
Ollama + Llama 3.2 model (llama3.2:latest)

Tip: Llama 3.2 in Ollama has small variants (1B/3B). If your PC is weak, local AI still works — just expect slower responses.

Install & setup (Tested on Windows)
Install sqlmap.py
Install Ollama + Llama 3.2
Run SKYNET dashboard
First scan tutorial
Enable MCP server (agent mode)
Cloud AI setup (if your PC is weak)
Config power tips (config.py)
Full project structure map
Artifacts
Community & Resources
🤝 Contributing
📜 License
⚠️ Disclaimer

Install & setup (Tested on Windows)

1) Clone project

Open PowerShell:

git clone https://github.com/drcrypterdotru/sqlmap-skynet
cd sqlmap-skynet

2) Create venv (required)

py -3.12 -m venv .venv
.\.venv\Scripts\Activate.ps1
python --version

3) Install Python dependencies

pip install -U fastapi uvicorn python-dotenv aiohttp requests numpy scikit-learn

Optional (for MCP server over HTTP):

pip install -U fastmcp

4) Create `.env`

Copy-Item .env.example .env

Open .env and set what you need:

Local AI: Ollama (recommended)
Or Cloud AI keys (if PC is weak)

Install sqlmap.py

Best practice (inside project folder):

git clone https://github.com/sqlmapproject/sqlmap.git sqlmap

✅ You must have:

sqlmap-skynet\
  sqlmap\sqlmap.py
  main.py
  config.py
  ...

Install Ollama + Llama 3.2

1) Install Ollama (Windows)

Download & install Ollama for Windows from the official site.
After install, open a new PowerShell and verify:

ollama --version

2) Pull the model used by SKYNET (default)

Your config.py sets:

OLLAMA_MODELS['default'] = 'llama3.2:latest'

Pull it:

ollama pull llama3.2:latest

Test:

ollama run llama3.2:latest

If you want to switch model later, edit config.py under OLLAMA_MODELS.

Run SKYNET dashboard

Start the server:

python main.py --host 0.0.0.0 --port 1337 --debug

Open:

Dashboard: http://127.0.0.1:1337

First scan tutorial

Use a target you own / have permission to test.

Step 1 — Add targets

In the dashboard:

Paste one URL or multiple URLs (one per line)

Step 2 — Set method

GET for query params
POST for login/forms

Step 3 — Add session context (optional)

Headers (User-Agent, Authorization)
Cookies (session token)
POST body (for POST scans)

Step 4 — Start

Choose safe defaults:

max_cycles = 30
RAG = ON (recommended)
Web Search = OFF (start simple)

Then click Start.

Step 5 — Review

After the scan:

Check sqlmap_reports\
Check memory\sessions\ (if RAG enabled)

Enable MCP server (agent mode)

What MCP does

MCP allows an AI agent to call SKYNET tools:

sqlmap_scan → starts scan (uses sqlmap.py)
get_scan_status → polling status/results
get_ai_providers → shows available AI backends

1) Install MCP dependency (once)

pip install -U fastmcp

2) Start MCP server over HTTP

Run in a new PowerShell (keep dashboard in another terminal if you want both):

fastmcp run .\mcp\server.py:mcp --transport http --host 127.0.0.1 --port 8055

Your MCP endpoint:

http://127.0.0.1:8055/mcp

3) Confirm MCP tools

fastmcp list http://127.0.0.1:8055/mcp

Does MCP work with Autonomous AI?

✅ Yes. MCP tool sqlmap_scan creates SQLMapRunner("sqlmap.py"), and the runner uses core/autonomous_ai.py.
So MCP calls automatically use your Autonomous AI + RAG logic (no module changes needed).

MCPFast (FastMCP) CLI Mode — No UI (Headless / Server-Friendly)

Sometimes you don’t want the dashboard (example: Linux server / VPS / headless box).
You can run SKYNET fully from command line using FastMCP — perfect when you don’t have a GUI.

📸 MCPFast Demo Screenshots

1) Start MCP server (HTTP)

Open terminal in project root:

fastmcp run .\mcp\server.py:mcp --transport http --host 127.0.0.1 --port 8055

MCP endpoint:

http://127.0.0.1:8055/mcp

2) Verify MCP is working (Tools + Providers + Status)

Run these commands (this is the fastest proof MCP is healthy):

fastmcp list http://127.0.0.1:8055/mcp --auth none

fastmcp call http://127.0.0.1:8055/mcp get_ai_providers --auth none
fastmcp call http://127.0.0.1:8055/mcp get_scan_status --auth none

What you should see

Tools (3): sqlmap_scan, get_scan_status, get_ai_providers
Providers: ollama (recommended)
Status: running=false until you start a scan

3) Run scan by URL (one target)

fastmcp call http://127.0.0.1:8055/mcp sqlmap_scan --auth none ^
  url="http://testphp.vulnweb.com/artists.php?artist=1" ^
  method="GET" ^
  max_cycles=10 ^
  ai_provider="ollama"

4) Run scan by target list file (many targets)

Create target.txt in project root (one URL per line), then:

fastmcp call http://127.0.0.1:8055/mcp sqlmap_scan --auth none ^
  targetlist="target.txt" ^
  method="GET" ^
  max_cycles=10 ^
  ai_provider="ollama"

Tip: targetlist should be inside the project folder for safety.

Cloud AI setup (if your PC is weak)

If you can't run Ollama fast (low RAM/CPU), you can use Cloud AI.

Quick setup

Open .env
Add ONE key (example OpenAI):

OPENAI_API_KEY=YOUR_KEY_HERE

Other supported keys:

GROQ_API_KEY=...
DEEPSEEK_API_KEY=...
KIMI_API_KEY=...
ANTHROPIC_API_KEY=...

SKYNET will detect which providers are available and use them when needed.

Provider priority is defined in config.py as AI_PRIORITY.

Config deep guide (config.py)

config.py is the brains + intel database of SKYNET.
If you want better results, this is the first file to tune.

Tip: On GitHub you can click the file: config.py → then use the browser search (Ctrl+F) for the section names below.

Quick map (what you can tune)

✅ AI models (Ollama model names)
✅ AI provider priority (Ollama first or cloud first)
✅ WAF bypass packs (delay/threads/tamper presets)
✅ Tamper script database (profiles by aggressiveness)
✅ Technique priority (faster signal, less noise)
✅ Keyword intelligence (High-Value Columns (Keyword Intel): Customize the keyword list to match your environment (examples: crypto, email, password, hash, token, apikey, secret). SKYNET will prioritize and highlight matching columns so you can identify impact faster.)

⚠️ For educational/authorized testing only. Use only on systems you own or where you have written permission.

Click for full details (config.py)

1) OLLAMA_MODELS — local model selection

SKYNET reads the local Ollama model name from:

OLLAMA_MODELS = {"default": "llama3.2:latest"}

What to do:

Keep llama3.2:latest as default if it works well on your PC.
If your PC is slow, use a smaller model (if you have it in Ollama) and set it here.

Why it matters: model choice affects speed + quality of AI plans.

2) AI_PRIORITY — which AI provider is used first

Example:

AI_PRIORITY = ["ollama","deepseek","kimi","groq","openai","claude"]

What to do:

Want offline-first? keep ollama first.
Want cloud-first? move your cloud provider to the front.

Why it matters: the first available provider in this list is used most often.

3) REAL_WORLD_BYPASSES — WAF/Block presets (very important)

This section is like a “bypass playbook”.
It stores presets for common conditions like:

Cloudflare / Akamai / Imperva / Sucuri
ModSecurity / AWS WAF
Generic 403 / rate limiting / captcha pages

Typical preset contains:

delay and timing
threads (lower threads = less blocks)
tamper_scripts (safe → aggressive)

How SKYNET uses it:

if it detects blocking patterns (403, suspicious headers, WAF signatures), it can propose one of these presets automatically.

Operator tip:

Start with low/no tamper + low threads.
Increase only if you are authorized and you confirm WAF behavior.

4) TAMPER_SCRIPTS — tamper profiles (basic → aggressive)

Tamper scripts can help evade filters but they can also:

slow scans
increase false positives
increase detection/noise

SKYNET organizes them into groups like:

basic
moderate
aggressive
waf_specific

Best practice:

Use basic first.
Use moderate only when blocked.
Use aggressive only with permission + clear block evidence.

5) TECHNIQUE_PRIORITY — faster signal with less noise

This controls which SQLi techniques SKYNET tries to focus on first (depending on your logic).
Typical techniques:

Error-based
Union-based
Boolean-based
Time-based

Why it matters:
On many real targets, hammering time-based from the start creates slow scans + blocks.
A good priority order can produce faster confirmation and cleaner results.

6) HIGH_VALUE_COLUMNS — keyword intel that improves result quality

This is one of the strongest “operator experience” features.

HIGH_VALUE_COLUMNS contains keywords like:

password, pass, hash
email, phone
token, apikey, secret
session, cookie
card, cc, billing
admin, role, permission

How SKYNET uses it:

When enumeration finds many columns, SKYNET highlights likely high-value fields.
This makes results easier to read and reduces time wasted on irrelevant tables.

Customize it for your environment Add your own keywords, for example:

HR: employee_id, salary, department
Finance: invoice, receipt, balance
E-commerce: order_id, shipment, cart
Your apps: telegram, line_id, customer_uid

Big win: better keyword intel = faster identification of “real impact” in a report.

7) Other useful knobs you may have in config.py

Depending on your version, you may also see:

default delays/threads limits
retry policy / backoff
request headers templates
detection patterns / error signature lists
file browser root restrictions

If you’re unsure: keep defaults and tune slowly.

“More Advanced Feature and Config” (recommended way)

If you want users to click from README:

Add a GitHub link in your repo to config.py (example):
- ./config.py
In GitHub, it becomes clickable automatically.

Example you can paste anywhere in README:

See: [config.py](./config.py)

Full project structure map

sqlmap-skynet/
├─ 🚀 main.py
│  └─ FastAPI dashboard server + WebSocket control + scan orchestration
├─ 🧠 config.py
│  └─ AI models + provider priority + WAF bypass packs + tamper DB + keyword intel
├─ 🧩 api.py
│  └─ (optional/legacy) stats endpoint helper (older Flask-based helper)
├─ 🧪 .env / .env.example
│
├─ 🛠️ scanners/
│  ├─ 🧭 runner.py
│  │  └─ Phase engine (detect → bypass → enumerate → dump) + session state
│  └─ 🧱 sqlmap_backend.py
│     └─ Builds and executes real `sqlmap.py` commands
│
├─ 🧠 core/
│  ├─ 🤖 autonomous_ai.py
│  │  └─ AI planner + provider health + JSON plan parsing
│  ├─ 🧬 rag_memory.py
│  │  └─ Store/recall session intelligence (learn from past scans)
│  ├─ 🧾 report_generator.py
│  │  └─ Build artifacts/reports from results
│  ├─ 📡 state_manager.py
│  │  └─ Shared runtime state (running/progress/results)
│  ├─ 🛡️ waf_intel.py
│  │  └─ WAF fingerprinting + mutation ideas + learning profile
│  └─ 🧿 debug_logger.py
│     └─ Colored logs + structured tags
│
├─ 🔎 search/
│  └─ 🌐 web_search.py
│     └─ Optional web intel lookup + caching
│
├─ 🔌 mcp/
│  └─ 🛰️ server.py
│     └─ MCP tool server (sqlmap_scan, get_scan_status, get_ai_providers)
│
├─ 🧰 utils/
│  ├─ 🗂️ file_browser.py
│  │  └─ Safe file browsing inside project root (UI file picker)
│  └─ 🧩 parsers.py
│
├─ 🎛️ templates/
│  └─ 🖥️ dashboard.html
├─ 🎨 static/
│  └─ ⚙️ js/stats.js
│
├─ 🧠 memory/
│  ├─ 🧾 sessions/
│  │  └─ RAG memory snapshots (per scan)
│  ├─ 🗃️ search_cache/
│  │  └─ cached web intel queries
│  └─ 🧠 *_patterns.json
│     └─ learned patterns + bypass intelligence
│
└─ 📦 sqlmap_reports/
   └─ generated scan reports (html/json/txt depending on run)

Artifacts

Where your outputs go:

sqlmap_reports\ → scan reports (proof + export)
memory\sessions\ → RAG snapshots per session
memory\search_cache\ → cached web intel results
memory\*_patterns.json → learned patterns and bypass info

Star History

Community & Resources

Join Telegram: https://t.me/burnwpcommunity

Website: https://drcrypter.net
More tools, resources, and updates are shared on the website + community.

🤝 Contributing

We welcome contributions! Feel free to fork this repository, make enhancements, and open pull requests. Please check the issues page for ongoing tasks or bug reports.

📜 License

This project is licensed under the MIT License. See the LICENSE file for details.

⚠️ Disclaimer

This tool is for educational purposes only. 🏫 The creator and contributors are not responsible for any misuse or damages caused. Use responsibly, and only on systems you own or have permission for. ✅

Discover MCPs & agents

sqlmap-skynet

Links

README