Decepticon — Autonomous Red Team Agent

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

Install

Prerequisites: Docker and Docker Compose v2. Supported on macOS (Apple Silicon + Intel), Linux (amd64 + arm64), and Windows (amd64 + arm64) — native via PowerShell or via WSL2 (Ubuntu / Kali).

macOS / Linux / WSL2

curl -fsSL https://decepticon.red/install | bash
decepticon onboard   # Interactive setup wizard (provider, API key, model profile)
decepticon           # Start the core stack and drop into the terminal CLI

The default start brings up the core management plane (LiteLLM, PostgreSQL, Neo4j, Skillogy, LangGraph, sandbox) and launches the terminal CLI. Specialist workloads (BloodHound CE, Sliver C2, Ghidra MCP, …) and the web dashboard come up on demand — the orchestrator spawns specialists via ops_start("ad") etc., and you bring up the dashboard from inside the CLI with /web (see Web Dashboard).

Windows (PowerShell, native)

irm https://decepticon.red/install.ps1 | iex
decepticon onboard
decepticon

→ Quick start · Full setup walkthrough

Use as a library (pip)

Building on top of the agents — a SaaS, a research integration, or a custom orchestrator? Install the SDK from PyPI:

pip install decepticon              # core SDK
pip install "decepticon[neo4j]"     # + the knowledge-graph attack-chain tools

decepticon is a client SDK: it ships the agent factories, middleware, tools, and skills, and routes LLM calls and sandbox execution to runtime services over HTTP (DECEPTICON_LLM__PROXY_URL, SAAS_SANDBOX_URL). Running agents still needs those services — use the Docker stack above, or point the URLs at your own equivalents. See Decepticon as a library for the factory override surface, declarative PluginBundle plugins, and the safety gate.

💖 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.

Benchmark

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

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

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.

Architecture

Two-network design. The always-on management plane (LiteLLM, PostgreSQL, Skillogy, LangGraph) and the always-on sandbox plane stay up across the whole engagement; everything else is dynamic-spawn — the Web dashboard comes up on /web from the CLI, and specialist workloads (BloodHound CE, Sliver C2, Ghidra MCP, …) come up only when the orchestrator calls ops_start(...) (see ADR-0006). Networks: management on decepticon-net; sandbox + C2 server + 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

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

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.

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

Documentation

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

Community

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

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.

License

Apache-2.0