BitterSecurity/Decepticon
Autonomous Hacking Agent for Red Team
Decepticon – Autonomous Red‑Team Agent
What it is – Decepticon is an open‑source, container‑based platform that lets a large‑language‑model‑driven agent run realistic offensive security engagements. It orchestrates a suite of specialist agents (recon, exploitation, post‑exploitation, etc.) that operate inside an isolated Kali Linux sandbox, generate full engagement documentation (ROE, OPPLAN, MITRE ATT&CK mapping), and interact with real tools such as nmap, msfconsole, Sliver, Ghidra, and BloodHound.
Why it matters – Most “AI‑hacker” demos stop at a single scan. Decepticon goes further: it follows multi‑step attack chains, maintains persistent interactive sessions, and isolates everything in a two‑network Docker setup (management plane + sandbox plane). The project is positioned as an “offensive vaccine” – using autonomous red‑team findings to improve defenses.
Key components
| Component | Role |
|---|---|
| Core stack (LiteLLM, PostgreSQL, Neo4j, LangGraph, Skillogy) | Manages LLM routing, stores findings, runs the graph‑based knowledge base, and coordinates agent workflows. |
Kali sandbox (Docker network sandbox‑net) |
Executes all commands/tools in a hardened, isolated environment separate from the management plane. |
| Specialist workloads (BloodHound CE, Sliver C2, Ghidra MCP, etc.) | Spawned on demand via the orchestrator (ops_start(...)). |
| Web dashboard | Optional UI launched from the CLI (/web). |
Python SDK (pip install decepticon) |
Lets developers embed the agent factories, plugins, and safety gate into their own codebases. |
Model profiles (eco, max, test) |
Tiered LLM selection with provider fallback (Anthropic, OpenAI, Gemini, Ollama, etc.). |
Quick start (Docker‑compose)
# macOS / Linux / WSL2
curl -fsSL https://decepticon.red/install | bash
# Interactive onboarding – set provider API keys, choose a model profile
decepticon onboard
# Launch the full stack and drop into the terminal CLI
decepticon
Windows uses the same flow via PowerShell.
The CLI starts the always‑on management services; additional tools appear only when the orchestrator requests them. From the CLI you can open the optional web UI with /web.
Using Decepticon as a library
pip install decepticon # core SDK
pip install "decepticon[neo4j]" # add knowledge‑graph helpers
The SDK provides agent factories, a plugin bundle system, and a safety gate that forwards LLM calls and sandbox commands to the running services (configured via DECEPTICON_LLM__PROXY_URL and SANDBOX_URL).
Benchmarks & performance
| Benchmark | Difficulty | Pass rate |
|---|---|---|
| XBOW validation (Level 1) | Easy | 100 % (45/45) |
| XBOW validation (Level 2) | Medium | 98 % (50/51) |
| XBOW validation (Level 3) | Hard | 87.5 % (7/8) |
| Overall | All levels | 98.08 % (102/104) |
Full per‑challenge results, attack‑class matrix, and LangSmith traces are linked in the repo.
Typical use cases
- Red‑team automation – Run end‑to‑end penetration tests without manually chaining tools.
- Security research – Prototype new attack techniques by extending the agent roster or adding custom skills.
- Defensive training – Generate realistic adversary emulation data for blue‑team exercises.
- Product integration – Embed the SDK into SaaS platforms that need autonomous security assessments.
License & community
- License: Apache‑2.0 (permissive, commercial‑friendly).
- Support: Sponsorship via GitHub Sponsors, Discord community, live hosted app (
app.decepticon.red). - Documentation: Extensive docs covering installation, architecture, agent roster, model configuration, and the “offensive vaccine” feedback loop.
Bottom line
Decepticon is a fully‑featured, production‑grade framework for autonomous red‑team operations. It combines LLM‑driven decision making with real security tooling, sandbox isolation, and a knowledge‑graph backend, making it one of the most complete open‑source AI‑pentest agents available today.
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