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What is DarkMoon?

DarkMoon is an open-source AI penetration testing platform. Point it at a target you are authorized to test, and it runs the whole assessment on its own: it reasons, plans, and dispatches 50 specialist agents that execute real offensive operations through a controlled MCP layer, then reports every vulnerability with the exact command, the raw output and the proof behind it.

It does not replace the pentester. It clears the repetitive part of an assessment with evidence, so your experts spend their time on judgment, not toil.

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Why DarkMoon?

• 🌐 Beyond the web — one agentic system across web, APIs, Active Directory, Kubernetes, cloud (AWS, Azure, GCP), CI/CD, databases and IoT firmware, chained end to end
• 🕶️ Your data never reaches the LLM — reversible local tokenization turns real IPs, hostnames, credentials and paths into deterministic placeholders (IP_PRIVATE_001, ...), rehydrated locally only at the moment a tool runs and masked back out of every result. Runs fully on a local model. Exfiltration is blocked. This is the part no cloud competitor has.
• 🧾 Proof, not scores — every finding ships with the exact command and raw output, reproducible, so there is nothing to trust blindly and almost nothing to triage
• 🔧 50 specialist agents, 50+ offensive tools — Nuclei, NetExec, BloodHound, sqlmap, Impacket, Naabu, httpx, ffuf and more, orchestrated over MCP
• 🛡️ Security by design — the AI never runs a command directly; every action flows through a controlled, logged MCP interface
• ♾️ CI/CD native — trigger a pentest in the pipeline and get findings as artifacts
• 📖 Nothing hidden — GPLv3, self-hosted, each agent's methodology is plain Markdown you can read, diff and fork

Built for security teams, DevSecOps engineers, red teamers and ethical hacking professionals.

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📊 Benchmark: 57 real vulnerabilities on OWASP Juice Shop

Real, reproducible black-box run against OWASP Juice Shop on a local LLM (nothing leaves your infrastructure):

Metric	Result
Vulnerabilities found	57 (8 critical / 24 high / 21 medium / 4 low)
Wall-clock time	28.5 min
Proof-of-exploitation	per finding
LLM	local (Ollama / llama.cpp)

Reproduce it and compare tools yourself: ASCIT31/Darkmoon-Benchmarks.

🆚 How DarkMoon compares

	DarkMoon	strix	shannon	PentAGI
Runs on local LLM (data never leaves)	✅	❌ cloud	❌ cloud	partial
Privacy Gateway (local tokenization)	✅	❌	❌	❌
Active Directory + Kubernetes	✅	❌	❌	partial
Proof-of-exploitation	✅	✅	✅	✅
Open source	✅ GPL-3.0	✅	✅	✅

Compiled from public repos/docs (2026-08); corrections welcome via PR.

Quick Start

Prerequisites

• Docker & Docker Compose
• An LLM API key (OpenRouter, Anthropic, OpenAI, or local models)

Note: GPU configuration, NVIDIA driver troubleshooting, and advanced environment setup are covered in the Full Documentation, GPU Troubleshooting.

Installation

1. Clone the repository

git clone https://github.com/ASCIT31/Dark-Moon.git
cd Dark-Moon

2. Configure your LLM provider

install.sh handles provider configuration interactively, no need to edit docker-compose.yml:

./install.sh           # skip form if .opencode.env already configured
./install.sh --init    # force reconfiguration (cloud or local model)
./install.sh --help    # show usage

Supports cloud providers (Anthropic, OpenAI, OpenRouter…) and local models (Ollama, llama.cpp).

Note: For full details on environment variables and local model setup, see the Full Documentation, Environment Variables.

3. Build and launch

./install.sh  # Clean install with full stack reset

4. Run your first assessment

./darkmoon.sh "TARGET: example.com"

5. Monitor in real-time

./darkmoon.sh --log <session_id>

Note: Real-time session logs display every command executed by the MCP server. See Full Documentation, Session Logs for details.

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How It Works

DarkMoon operates as a strategic AI security agent orchestrator aligned with ISO 27001, NIST SP 800-115, and MITRE ATT&CK methodologies.

When you provide a target, the platform automatically:

1. 🔍 Discovers the target environment (ports, services, protocols)
2. 🧠 Fingerprints the technology stack (frameworks, CMS, APIs)
3. 🎯 Models the attack surface
4. 🚀 Deploys specialized sub-agents based on detected technologies
5. 🔬 Executes an intelligent vulnerability scanning loop with reactive adaptation
6. ✅ Validates findings with evidence (requests, payloads, responses)
7. 📝 Generates a structured audit report

Sub-Agent Orchestration

DarkMoon dynamically selects and dispatches specialized agents depending on the technologies discovered:

Detected Technology	Agent Triggered
WordPress, Drupal, Joomla, Magento, PrestaShop, Moodle	CMS-specific agent
PHP, Node.js, Flask, ASP.NET, Spring Boot, Ruby on Rails, Go	Stack-specific agent
GraphQL	GraphQL agent
Active Directory	AD agent
Kubernetes	Kubernetes agent
AWS, Azure, GCP	Cloud-provider agent
Entra ID (Microsoft identity)	Identity agent
GitHub, GitLab, Jenkins	SCM & CI/CD agent
Terraform, Ansible	Infrastructure-as-Code agent
Docker, container registries	Container agent
HashiCorp Vault	Secrets agent
PostgreSQL, MySQL, MSSQL, Oracle	Database agent
Redis, RabbitMQ, Kafka, MQTT	Messaging & cache agent
Firmware / IoT images	Firmware agent
Headless browser required	Headless browser agent

Multiple agents can execute in parallel across hybrid architectures.

Planes that require credentials to be meaningful (cloud accounts, CI/CD, secret stores, databases, Active Directory, Kubernetes) are never dispatched on inference. They fire only when a concrete artifact is found (a key, a token, a reachable metadata endpoint) or when you authorize them explicitly, and are otherwise flagged in the report.

Note: For the complete list of agents, their structure, lifecycle, and how to create custom agents, see Full Documentation, AI Agents.

Architecture Overview

User ──> DarkmoonCLI ──> OpenCode (AI Brain) ──> MCP (Security Gatekeeper) ──> Docker Toolbox (Real Tools)

sequenceDiagram
  participant U as User
  participant O as OpenCode
  participant A as AI Agent
  participant M as MCP Darkmoon
  participant T as Docker Toolbox

  U->>O: User prompt
  O->>A: Delegate task
  A->>M: MCP function call
  M->>T: Execute real tool
  T-->>M: Results
  M-->>A: Structured output
  A-->>O: Next decision
  O-->>U: Summary / result

The AI reasons and plans. The MCP controls what can be executed. The Toolbox runs isolated tools inside Docker. The AI never directly touches the system, this is security by design.

Note: For the full architecture breakdown (deployment diagrams, network flows, security boundaries), see Full Documentation, Architecture.

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Scope Definition

DarkMoon supports flexible scope definition directly from the command line.

Quick pentest (zero config):

./darkmoon.sh "TARGET: http://172.19.0.3:3000"

Bug bounty mode (flags activate automatically):

./darkmoon.sh "TARGET: http://172.19.0.3:3000 PROGRAM=\"Juice Shop\" FOCUS=sqli,xss,idor NOISE=moderate FORMAT=h1"

Key flags include FOCUS, EXCLUDE, CREDS, TOKEN, NOISE, SEVERITY, FORMAT, and more, all interpreted naturally by the AI.

Note: For the complete flags reference, asset types, EXCLUDE/FOCUS free-form syntax, and advanced multi-target scoping, see Full Documentation, Scope Definition.

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Integrated Toolbox

DarkMoon ships with a purpose-built Docker image containing 50+ security tools compiled and optimized in a multi-stage build:

Category	Tools (examples)
Port scanning	Naabu (discovery), nmap (targeted service probes)
Web scanning	Nuclei, ffuf, dirb, sqlmap, Arjun, wafw00f
Recon & crawling	Subfinder, Katana, Waybackurls, httpx
CMS	WPScan, CMSeeK, WhatWeb
Active Directory	NetExec, BloodHound, Impacket (30+ scripts)
Kubernetes	kubectl, Kubescape, Kubeletctl, kube-bench, rbac-police
Cloud CLIs	aws, az, gcloud, gsutil, bq
Databases & cache	psql, mysql, redis-cli, sqlite3
Firmware / IoT	binwalk, unsquashfs, sasquatch, firmwalker
Cracking	hashcat, john, 7z2john
Network	Hydra, curl, dig, SNMP tools
Browser	Lightpanda (headless)

All tools are directly accessible, no path configuration needed.

Note: For the complete tools list with installation details and how to add new tools, see Full Documentation, Toolbox.

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📖 Documentation Guide

DarkMoon's Full Documentation covers everything you need to operate the platform. Here is a quick reference to the most important sections:

Topic	What You'll Find	Link
GPU & Driver Setup	NVIDIA troubleshooting for Docker, WSL, and native Linux	GPU Guide
Environment Variables	LLM provider configuration, API keys, model selection	Environment Config
Startup & Build	install.sh behavior, docker compose build, stack management	Build & Launch
Scope & Flags	TARGET syntax, bug bounty mode, FOCUS/EXCLUDE, credentials	Scope Definition
Assessment Workflow	Step-by-step: discovery, fingerprinting, agents, reporting	Assessment Engine
Real-Time Session Logs	Monitor commands executed by the MCP server live	Session Logs
AI Agents	Agent structure, lifecycle, how to create or modify agents	AI Agents
Architecture	Deployment diagrams, security boundaries, execution flow	Architecture
Toolbox	Complete tool list, adding tools, Docker image internals	Toolbox
MCP Workflows	Workflow structure, creating custom workflows, best practices	MCP Workflows
Available Tools List	Full table of 50+ tools with paths and sources	Tools List
Training Labs	Recommended vulnerable labs to train DarkMoon	Pentester Labs

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Use Cases

DarkMoon is designed as a versatile security testing platform for:

• 🔒 Security teams, run continuous automated penetration testing across your infrastructure
• ⚙️ DevSecOps pipelines, integrate AI-driven security research into CI/CD workflows
• 🎯 Bug bounty hunters, accelerate ethical hacking with autonomous target analysis
• 🔬 Security researchers, explore attack surfaces with an AI cybersecurity platform that adapts in real time
• 🎓 Training & education, learn offensive security with guided, reproducible assessments

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Example Prompts

# Web application pentest
./darkmoon.sh "TARGET: http://172.19.0.3:3000"

# Active Directory assessment
./darkmoon.sh "TARGET: 192.168.1.10"

# Bug bounty with specific focus
./darkmoon.sh "TARGET: https://app.example.com PROGRAM=\"Example BB\" FOCUS=sqli,rce,ssrf EXCLUDE=H1 FORMAT=h1"

Note: For more prompt examples including DVGA, Juice Shop, and headless browser scenarios, see Full Documentation, Prompt Examples.

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Contributing

DarkMoon is open source and welcomes contributions. Whether you want to add new agents, integrate tools, create workflows, or improve documentation, see CONTRIBUTING.md for guidelines.

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License

This project is licensed under the GNU General Public License v3.0. See LICENSE for details.

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