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Offensive Z Wave

CommunityPopular
SnailSploit
offensive-z-wave

Z-Wave attack methodology — sniffing with Z-Force / EZ-Wave / RTL-SDR + ZniffMobile, S0 (legacy) network-key derivation flaw and key reuse, S2 (modern) ECDH commissioning analysis, replay/injection on unauthenticated nodes, default-key brute-force on test deployments, and home-automation hub pivots. Use when targeting Z-Wave smart home devices (door locks, sensors, garage controllers) — common in mid-2010s smart home deployments still in production.

Overview

PublisherSnailSploit
RepositoryClaude-Red
Skill nameoffensive-z-wave
Stars
7.4K
Forks
967
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by SnailSploit on GitHub. Read the source before you install it.

Installation

Install the Offensive Z Wave AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/SnailSploit/Claude-Red/tree/main/Skills/wireless/offensive-z-wave
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/SnailSploit/Claude-Red.git /tmp/Claude-Red
mkdir -p .claude/skills
cp -r /tmp/Claude-Red/Skills/wireless/offensive-z-wave .claude/skills/offensive-z-wave
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Offensive Z Wave in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Offensive Z Wave on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Offensive Z Wave is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Z-Wave Attacks

Z-Wave runs in the 800/900 MHz ISM band (US: 908 MHz, EU: 868 MHz). Older networks used the S0 security scheme with a fixed-derivation network key — long-known to be flawed. S2 (mandatory for Z-Wave Plus v2 since 2017) uses ECDH commissioning and is significantly stronger.

Quick Workflow

  1. Identify region (US 908 MHz / EU 868 MHz) — adapter frequency must match
  2. Sniff inclusion (commissioning) traffic — that's where keys are exchanged
  3. Determine S0 vs S2 from frame format
  4. For S0: derive/replay; for S2: analyze ECDH and look for implementation flaws

Hardware

AdapterUse
Z-Force (legacy, hard to find)Original research tool
EZ-Wave (custom HackRF firmware)Modern, full transceiver
Aeotec Z-StickCommercial controller, useful as legitimate node
HackRF + open Z-Wave firmwareMulti-band SDR approach
RTL-SDR + ZniffMobile (passive only)Cheap sniffer

Sniffing

bash
# EZ-Wave (HackRF firmware-based)
git clone https://github.com/cureHsu/EZ-Wave
ezwave-sniff -f 908.4MHz -o capture.pcap

# Wireshark with the Z-Wave dissector parses captured frames
wireshark capture.pcap

Look for the inclusion phase (controller adding new device) — that's where the network key is exchanged.

S0 Security Flaw

S0 derives the network key from a fixed all-zero PSK during the inclusion of the first device. That fixed material is well-known — any S0 network you sniff during inclusion can be decrypted offline.

S0 commissioning:
  1. New node joins → controller sends key with zero-PSK encryption
  2. Attacker sniffs commissioning frame → derives session key
  3. All future S0 traffic on that network is decryptable

If you can:

  • Trigger inclusion (factory-reset a node, or wait for legitimate inclusion)
  • Sniff during the ~2-second key-exchange window

You own the network key for that mesh.

S2 (Z-Wave Plus / S2 Authenticated)

S2 fixes S0 by using ECDH for commissioning:

  • Each device has a Curve25519 keypair
  • Inclusion uses DSK (Device Specific Key) verified out-of-band (sticker/QR)
  • Network key never traverses the air in plaintext

S2 attack surface is mostly implementation:

  • Inclusion-mode-always-open (controller misconfig)
  • Firmware bugs in S2 verification
  • Side-channel on ECDH on resource-constrained chips
  • DSK printed on a sticker → physical access yields it

Replay / Injection on Unauthenticated Nodes

Many low-end Z-Wave devices (older sensors, basic switches) don't enforce S0 or S2 — they accept commands in cleartext.

python
# scapy-zwave (community fork) for crafted frames
from scapy.contrib.zwave import *
frame = ZWave(home_id=0x12345678)/ZWaveBasic(set_value=0xff)
sendp(frame, iface='ezwave0')

This unlocks doors / switches lights / unarms sensors when the target lacks authentication.

Key Brute-Force

For old test deployments using default home IDs / network keys:

bash
# Try default home IDs
for hid in 0x00000000 0x12345678 ...; do
  ezwave-test --home-id $hid --target-node 1
done

Hit rate on production is low; useful only for default-config IoT lab gear.

Hub Pivots

Z-Wave devices are typically controlled by a hub (SmartThings, Hubitat, Vera, Home Assistant, Z-Wave JS UI). The hub is a Linux device with the Z-Wave PSK in plaintext storage:

  • SmartThings Hub: previously cloud-only credentials; modern v3 stores network key locally
  • Home Assistant: ~/.homeassistant/zwave_js.json typically contains keys
  • Hubitat: web UI with default password on older versions

Compromise the hub → walk away with the Z-Wave PSK + every paired device's command authority. See offensive-iot for hub firmware extraction.

Engagement Cheatsheet

bash
# 1. Identify region + frequency
# US: 908.4 MHz; EU: 868.4 MHz; CN: 868.4 MHz

# 2. Sniff
ezwave-sniff -f 908.4MHz -o cap.pcap
wireshark cap.pcap   # filter zwave

# 3. Identify S0 vs S2 from frame format

# 4. For S0: capture inclusion → derive key → decrypt history + control devices

# 5. For S2: focus on hub compromise / DSK theft / implementation bugs

# 6. Test unauthenticated cleartext devices with crafted frames

Detection

  • Most Z-Wave deployments have no IDS comparable to Wi-Fi/Zigbee monitoring
  • Hub may log unexpected commands but UI rarely surfaces these to users
  • Inclusion-mode-open is visible in hub UI but ignored by inattentive admins

Reporting

  • Identify chipset / firmware revision per device (ZW0500 series, ZW7000 series)
  • Map S0 vs S2 per node — note any S0 left on a network with S2-capable nodes
  • Document hub compromise paths separately

Key References

Frequently asked questions

What does the Offensive Z Wave AI skill do?

Z-Wave attack methodology — sniffing with Z-Force / EZ-Wave / RTL-SDR + ZniffMobile, S0 (legacy) network-key derivation flaw and key reuse, S2 (modern) ECDH commissioning analysis, replay/injection on unauthenticated nodes, default-key brute-force on test deployments, and home-automation hub pivots. Use when targeting Z-Wave smart home devices (door locks, sensors, garage controllers) — common in mid-2010s smart home deployments still in production.

Why use Offensive Z Wave on TypingMind?

Because you install it once and use it with any model. Offensive Z Wave is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Offensive Z Wave in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/SnailSploit/Claude-Red/tree/main/Skills/wireless/offensive-z-wave. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Offensive Z Wave?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Offensive Z Wave?

As many as you like. As long as a model supports skills, you can use Offensive Z Wave with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Offensive Z Wave AI skill free?

Yes. It is published on GitHub by SnailSploit under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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