decepticon 1.1.0

AI-powered autonomous red team testing framework

Decepticon — Autonomous Red Team Agent

"Another AI hacker? Let us guess — it runs nmap and writes a report."

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Install

Prerequisites: Docker and Docker Compose v2. Supported on macOS (Apple Silicon + Intel), Linux (amd64 + arm64), and Windows via WSL2 (Ubuntu or Kali). Native Windows is not supported — install WSL2 first, then run the commands below from inside the WSL shell.

curl -fsSL https://decepticon.red/install | bash
decepticon onboard   # Interactive setup wizard (provider, API key, model profile)
decepticon           # Start everything: terminal CLI + web dashboard at http://localhost:3000

→ Quick start · Full setup walkthrough

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💖 Support Decepticon

We're building Decepticon toward an Offensive Vaccine for the AI-driven threat landscape. If you believe in autonomous red teaming as a path to stronger defense, consider supporting the project.

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Benchmark

Benchmark	Difficulty	Pass Rate
XBOW validation-benchmarks	Easy (Level 1)	45 / 45 (100 %)
XBOW validation-benchmarks	Medium (Level 2)	50 / 51 (98.0 %)
XBOW validation-benchmarks	Hard (Level 3)	7 / 8 (87.5 %)
XBOW validation-benchmarks	All levels	102 / 104 (98.08 %)

• Full per-challenge index, attack-class matrix, and LangSmith traces
• Comparison vs other AI pentest agents (Strix, PentestGPT, MAPTA, Cyber-AutoAgent, XBOW commercial, …)

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What is Decepticon?

The "AI + hacking" space is full of demos that run nmap and print a report. That's not what this is.

Decepticon is a professional autonomous Red Team agent. It executes realistic attack chains — reconnaissance, exploitation, privilege escalation, lateral movement, C2 — the way a real adversary would, not the way a scanner does.

But more importantly: it operates under the discipline that separates red teamers from script kiddies. Before a single packet leaves the wire, Decepticon generates a complete engagement package — RoE, ConOps, Deconfliction Plan, and OPPLAN with MITRE ATT&CK mapping — and every action runs inside those defined rules.

→ Engagement workflow deep dive

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Why Decepticon?

Real kill chains, not checkbox scans. Decepticon reads an OPPLAN and pursues objectives through whatever path opens up — pivoting, adapting, chaining techniques.

Interactive shells, actually. Real offensive tools are interactive (msfconsole, sliver-client, evil-winrm). Decepticon runs every command inside persistent tmux sessions with automatic prompt detection — so when a tool drops into an interactive prompt, the agent sends follow-up commands without workarounds.

Hardened sandbox isolation. All commands run inside a Kali Linux sandbox on a dedicated operational network (sandbox-net), separate from the management plane (decepticon-net). LangGraph drives the sandbox via the Docker socket. → Architecture

Offense serves defense. The planned Offensive Vaccine loop will turn findings into defense improvements through an attack → defend → verify cycle.

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Architecture

Two-network design — management services (LiteLLM, PostgreSQL, LangGraph, Web) on decepticon-net; sandbox, C2 server, and targets on sandbox-net. Neo4j is dual-homed so the agent (on management) can persist findings written from inside the sandbox.

→ Architecture deep dive · Knowledge graph

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Agents

16 specialist agents organized by kill chain phase, with a fresh context window per objective — no accumulated noise.

Orchestration · Reconnaissance · Exploitation · Post-Exploitation · Vulnerability Research · Domain Specialists (AD, Cloud, Smart Contracts, Reversing, Analyst).

→ Full agent roster and middleware stack

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Models & Providers

Tier-based, credentials-aware fallback chain. You declare which credentials you have in priority order; Decepticon builds the primary→fallback chain at every tier from there.

Profile	Tier per agent	Use case
eco (default)	Per-agent (HIGH for orchestrator/exploiter/patcher/analyst, MID for execution, LOW for recon/soundwave)	Production
max	Every agent on HIGH	High-value targets
test	Every agent on LOW	Development / CI

Tier-mapped providers: Anthropic, OpenAI, Google Gemini, MiniMax, DeepSeek, xAI, Mistral, OpenRouter, Nvidia NIM, Ollama (local). Subscription OAuth: Claude Max/Pro/Team, ChatGPT Pro/Plus/Team, Gemini Advanced, Copilot Pro, SuperGrok, Perplexity Pro.

Configure via decepticon onboard. → Full model reference & fallback examples

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Documentation

Topic	Doc
Installation and first engagement	Getting Started
Complete setup, OAuth, providers, dashboard	Setup Guide
All CLI commands and keyboard shortcuts	CLI Reference
All make targets	Makefile Reference
Agent roster and middleware	Agents
Model profiles and fallback chain	Models
Skill system and format spec	Skills
Web dashboard features and setup	Web Dashboard
System architecture and network isolation	Architecture
Neo4j knowledge graph	Knowledge Graph
End-to-end engagement workflow	Engagement Workflow
Offensive Vaccine loop	Offensive Vaccine
Contributing to Decepticon	Contributing

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Contributing

git clone https://github.com/PurpleAILAB/Decepticon.git
cd Decepticon
make dogfood  # Full OSS UX (launcher → onboard → CLI) on local code
make dev      # Backend hot-reload (compose watch) — daily dev loop

→ Contributing guide

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Community

Join the Discord — ask questions, share engagement logs, discuss techniques.

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Disclaimer

Do not use this project on any system or network without explicit written authorization from the system owner. Unauthorized access to computer systems is illegal. You are solely responsible for your actions. The authors and contributors assume no liability for misuse.

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License

Apache-2.0

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