DarkMoon

Open-source autonomous AI penetration testing — finds, exploits and qualifies every vulnerability on your own infrastructure

⭐ Star DarkMoon · 🚀 Quick Start · 🧩 Integrations · 📊 Benchmark · 🔒 Darkmoon Pro · ▶️ Watch the demo (Pro)

DarkMoon is autonomous AI penetration testing for your own infrastructure. Point it at an authorized target and it runs the whole assessment on its own, then documents every finding with the exact command and raw output.

• 🟢 Truly open source. GPLv3 and self-hosted, every agent's methodology is plain Markdown you can read, diff and fork.
• 🎯 Finds AND proves. Each vulnerability ships with the exact command and raw output (exploitation is agent-asserted; the Pro remediation retest is the machine-verified step), so there is almost nothing to triage.
• 🔒 Runs on a local LLM + Privacy Gateway. The gateway tokenizes your real IPs, hosts and credentials locally, so the model reasons on placeholders while real values stay on your perimeter.
• 🧩 Everywhere you build. Run it from GitHub, GitLab, Jenkins, VS Code, JetBrains, n8n, Grafana or Splunk — the open-source edition works with the CLI-based ones. See the integrations ↓

Kick off a run from the CLI. One TARGET line and DarkMoon takes over the whole assessment.	Autonomous agents reason and exploit, live in your terminal. Here a sub-agent flags CVE-2019-9978 and pivots straight to exploitation.
Recon and environment model. DarkMoon fingerprints the stack and confirms the attack surface before it strikes.	Live MCP stream. Every command the agent runs and its raw output, timestamped, with ./darkmoon.sh --log <session>.

Signal to agent dispatch. DarkMoon decides which specialist agents to deploy from exactly what it detects on the target.

As featured in Help Net Security · Cyber Security News · DevOps.com · SecurityBrief · LinuxLinks · IT Brief · ChannelLife

Quick Start

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

install.sh configures your LLM provider interactively (no need to edit docker-compose.yml) and builds the full stack:

./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). Then run your first assessment and watch it live:

./darkmoon.sh "TARGET: example.com"
./darkmoon.sh --log <session_id>

Prerequisites: Docker & Docker Compose, and an LLM API key (or a local model). GPU setup, environment variables and the full flags reference live in the Full Documentation.

Feature grid

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

🧩 DarkMoon everywhere

Run DarkMoon where you already build — in CI/CD, your IDE, automation and SecOps. Integrations marked OSS + Pro work with the open-source edition (local CLI + JSON); the Pro-only ones consume the DarkMoon Pro REST API (live dashboard, remediation→PR).

Every integration emits safe metadata only (severity, status, MITRE, ids) — never evidence, secrets or tokens. Full guides on docs.dark-moon.org; source under ASCIT31.

🔒 Darkmoon Pro — paid edition: web dashboard and automated remediation

🔒 These are Darkmoon Pro features (paid). The open source edition is the CLI shown above; the web dashboard and the automated remediation to pull requests are not in the open source build.

The screenshots below are the paid Darkmoon Pro web dashboard. They are not part of the open source repository you clone. The open source edition is the command line tool shown in the section above.

Pro: live web dashboard. Projects, targets and campaigns at a glance, with every finding bucketed by severity.	Pro: findings with CVSS and ATT&CK mapping. Each finding carries CVSS, MITRE ATT&CK and ISO 27001 mapping with an EXPLOITED status backed by a real exploit.
Pro: exportable web reports. A management summary in plain language plus full technical detail, exportable to Markdown and PDF.	Pro: orbital attack-surface map. A live 3D graph of nodes, connections and per-node vulnerabilities, with the MITRE attack path walked across the whole target, and **every finding linked to its proposed remediation as a reviewed pull request**. Renders in the browser and in VR.
Pro: scheduled recurring runs. Recurring automated campaigns with FOCUS, severity and recurrence built into the web dashboard.	Pro: campaign view. Every vulnerability in a campaign with its severity and confirmation status.

🔒 Pro: from finding to fix (automated remediation, paid)

🔒 Paid Pro feature. The open source engine finds and proves vulnerabilities. Automated remediation into pull requests is available only in Darkmoon Pro.

In the paid Pro tier, every finding flows finding → sandbox-validated fix → human-reviewed pull request, retested against the original exploit, and is never auto-merged. On our OWASP Juice Shop demo run, the Pro remediation agent fixed findings end-to-end with a clean live exploit-retest (a fix only counts when the original exploit is re-run and confirmed closed). See the remediation benchmark, the remediation agent docs and the 57 open PRs on ASCIT31/juice-shop.

▶️ Watch the demo (shows the Darkmoon Pro web dashboard)

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.

📊 Benchmark: 57 real vulnerabilities on OWASP Juice Shop

Real, reproducible black-box run against OWASP Juice Shop with a cloud frontier model (Anthropic Claude). DarkMoon also runs fully local (Ollama / llama.cpp) behind the Privacy Gateway — so nothing leaves your infrastructure; local-model coverage depends on the model you run.

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

🆚 How DarkMoon compares

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

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:

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.

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.

Integrated Toolbox

DarkMoon ships with a purpose-built Docker image containing 140+ security tools compiled and optimized in a multi-stage build:

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.

📖 Documentation Guide

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

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

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.

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.

License

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