RedAmon Turns 100 Security Tools Into an Open-Source AI Hacking Pipeline
A trending open-source framework chains reconnaissance, exploitation, and post-exploitation into one autonomous agent pipeline with human oversight gates.
- RedAmon is an MIT-licensed agentic red team framework trending on GitHub with 2,700+ stars.
- Chains reconnaissance, exploitation, post-exploitation, AI triage, and automated GitHub pull request fixes.
- Built on LangGraph, Neo4j knowledge graph, MCP tool servers, and 100+ integrated security tools.
- Supports OpenAI, Anthropic, Bedrock, OpenRouter, plus local Ollama and vLLM endpoints.
- Ships STRIDE threat model, non-disableable guardrails blocking government targets, and human-in-the-loop approval gates.
- Runs fully containerized via Docker Compose; requires 4-8 GB RAM minimum, 16 GB with OpenVAS.
RedAmon turns 100 security tools into an AI red-team pipeline
RedAmon has attracted thousands of GitHub stars with an open-source framework designed to coordinate an authorized security assessment from reconnaissance through a proposed code fix. Created by Samuele Giampieri and maintained with security researcher Ritesh Gohil, the project combines roughly 100 security tools, a LangGraph agent, a Neo4j attack-surface graph, and a web interface in one Docker Compose stack.
The MIT license makes the framework available for modification and self-hosting. Its broad scope explains much of the interest: RedAmon discovers assets, selects offensive tools, records attack paths, triages findings, edits source code, and opens a GitHub pull request. Human reviewers still decide whether a proposed patch is safe and ready to merge.
Red teaming simulates an attacker’s behavior under explicit authorization. RedAmon automates parts of that process with a large language model, so its value depends on target scope, model quality, tool configuration, and operator oversight. The project’s popularity establishes developer interest; repeatable benchmarks against skilled human testers would establish effectiveness.
Six components, one operating stack
RedAmon isolates its major services in Docker containers and connects them through APIs. This structure keeps scanners, agents, storage, and remediation logic separate while giving the orchestrator one shared view of the engagement.
| Component | Role |
|---|---|
| Reconnaissance pipeline | Runs subdomain discovery, port scanning, HTTP probing, resource enumeration, and vulnerability detection in parallel. |
| AI Agent Orchestrator | Uses LangGraph to query findings, choose tools, move through engagement phases, and accept operator instructions through chat. |
| Attack Surface Graph | Stores assets, findings, and relationships in Neo4j using 17 node types and more than 20 relationship types. |
| EvoGraph | Persists attack chains and prior observations across sessions to reduce duplicate work. |
| CypherFix | Triages graph findings, proposes source-code changes, and opens GitHub pull requests. |
| Project Settings Engine | Exposes more than 500 project-level controls through the web interface. |
From asset discovery to pull request
The documented workflow follows six stages:
- Reconnaissance: Kali-based containers discover hosts, services, URLs, and potential vulnerabilities.
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