DarkMoon
Open-source agent operations, security, and governance for security testing, mcp, penetration testing.
What does this agent do, and when should you use it?
The repository describes DarkMoon as: Autonomous AI pentesting engine, continuous offensive security across web, cloud, AD & Kubernetes. Agentic reasoning + real exploit execution deliver proof-based vulnerabilities. Privacy gateway: the LLM never sees your real IPs, hosts, creds or paths (deterministic placeholders rehydrated locally), nothing leaves your perimeter. This profile is a source-based catalog entry; an independent FARS review is still pending.
Autonomous AI pentesting engine, continuous offensive security across web, cloud, AD & Kubernetes. Agentic reasoning + real exploit execution deliver proof-based vulnerabilities. Privacy gateway: the LLM never sees your real IPs, hosts, creds or paths (deterministic placeholders rehydrated locally), nothing leaves your perimeter.
- Evaluate controls for operating or governing agent workloads.
- Test observability, security, or deployment behavior in a sandbox.
- Integrate documented controls into an existing operations stack.
What are this agent's strengths and limitations?
- Public source and README are available for inspection.
- Focused on security testing, mcp, penetration testing.
- Setup, model-provider support, and maturity must be confirmed against the current release.
- No independent FARS score has been assigned yet.
How do you install or deploy this agent?
Follow the current installation instructions in the [repository README](https://github.com/ASCIT31/Dark-Moon#readme). Requirements and provider setup vary by release.
How do you use this agent?
Start with the examples and quickstart in the [repository documentation](https://github.com/ASCIT31/Dark-Moon#readme), then test the workflow with limited permissions and non-sensitive data.