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Offensive Wifi Recon

CommunityPopular
SnailSploit
offensive-wifi-recon

Wi-Fi reconnaissance methodology — adapter selection, monitor mode and packet injection setup, regulatory domain handling, multi-band airspace mapping, hidden SSID discovery, BSSID/ESSID/channel/PMF/encryption fingerprinting, client probe analysis, vendor OUI lookup, war-driving with Kismet/airodump-ng/Wigle, and structured airspace data capture for downstream attacks. Use at the start of any wireless engagement to build the target map before active attacks; covers 2.4 GHz, 5 GHz, and 6 GHz (Wi-Fi 6E) bands and adapter compatibility for each.

Overview

PublisherSnailSploit
RepositoryClaude-Red
Skill nameoffensive-wifi-recon
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 Wifi Recon 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-wifi-recon
  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-wifi-recon .claude/skills/offensive-wifi-recon
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Offensive Wifi Recon 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 Wifi Recon 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 Wifi Recon 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.

Wi-Fi Reconnaissance

The first phase of any wireless engagement. Build a complete picture of the airspace before you deauth, evil-twin, or capture handshakes — every later attack depends on knowing the right BSSID, channel, encryption, and client population.

Quick Workflow

  1. Pick the right adapter for the target's band(s) and PHY
  2. Verify monitor mode + injection actually work
  3. Set the regulatory domain (legal channels and TX power)
  4. Sweep all bands passively
  5. Drill down on each in-scope BSSID for client population and PMF status
  6. Record everything in a structured target list before any active attack

Adapter Selection

ChipsetStrengthsNotes
Atheros AR9271 (Alfa AWUS036NHA)Solid 2.4 GHz monitor + injection802.11n only
Realtek RTL8812AU (AWUS036ACH)Dual-band, injectionDriver: aircrack-ng/rtl8812au
MediaTek MT7612U (AWUS036ACM)Stable dual-bandIn-tree driver on modern kernels
MediaTek MT7921AUWi-Fi 6 monitor (limited)Patched drivers required
AWUS036AXML / AXMWi-Fi 6E (6 GHz)Bleeding edge — verify per release
bash
# Identify your radio
lsusb | grep -iE "(atheros|realtek|mediatek|alfa)"
iw dev
iw list | grep -A 8 "Supported interface modes"
iw list | grep -E "Frequencies:" -A 30

Monitor Mode Setup

bash
# Kill conflicting services
sudo airmon-ng check kill

# Enable monitor mode
sudo airmon-ng start wlan0
# Or manually
sudo ip link set wlan0 down
sudo iw wlan0 set monitor control
sudo ip link set wlan0 up

# Verify monitor mode + injection
sudo aireplay-ng --test wlan0mon

The injection test should report 30/30 ack rates against nearby APs. Lower scores indicate driver, antenna, or position issues.

Regulatory Domain

bash
# Check current
iw reg get

# Set explicitly (us = United States, jp = Japan extended, etc.)
sudo iw reg set US

Setting the right regdomain unlocks legitimate channels (US: 1–11 on 2.4, 36–165 on 5; JP adds 12–13 + 184+ DFS) and TX power. Operate within the regdomain you're authorized to use.

Passive Multi-Band Sweep

bash
# All bands
sudo airodump-ng wlan0mon --band abg

# 5 GHz only (helps see UNII bands)
sudo airodump-ng wlan0mon --band a

# 6 GHz (requires 6E-capable adapter and updated airodump-ng)
sudo airodump-ng wlan0mon --band ax

# Hop only specific channels
sudo airodump-ng wlan0mon -c 1,6,11,36,40,44,48

Capture to file for later analysis:

bash
sudo airodump-ng wlan0mon --band abg --write recon --output-format pcap,csv

Targeted Capture

Once you've identified an in-scope BSSID:

bash
sudo airodump-ng -c 6 --bssid AA:BB:CC:DD:EE:FF -w target wlan0mon

Pin to the channel — channel-hopping during a focused capture loses frames.

Hidden SSIDs

Hidden APs broadcast beacons with empty ESSID. The name leaks during client probes (active scan) or association requests:

bash
# Wait for legitimate client to associate, ESSID appears in airodump output
# Or, if a client is already associated, deauth them once to force reassociation:
sudo aireplay-ng --deauth 1 -a AA:BB:CC:DD:EE:FF -c 11:22:33:44:55:66 wlan0mon

(Only deauth with explicit authorization — see offensive-deauth-disassoc.)

Kismet for War-Driving

bash
sudo kismet -c wlan0mon
# Open https://localhost:2501 for the dashboard

Kismet handles GPS integration, plots APs to a map, fingerprints by IE order, identifies probable IoT vendors from OUI prefixes, and tags known-vulnerable models.

For long-running captures, drop --no-ncurses and run headless under tmux.

Wigle Submission

If the engagement permits:

bash
# Export Kismet's .kismet → CSV → Wigle import format
kismetdb_dump_devices --in capture.kismet --out devices.csv

(Wigle aggregates wireless network observations geographically — useful for mapping but check ROE.)

Vendor / OUI Identification

bash
# Quick OUI lookup
echo "AA:BB:CC" | wireshark-tools/manuf-lookup
# Or check the airodump CSV's BSSID prefix against /usr/share/wireshark/manuf

Vendor identification informs:

  • Likely default credentials (router brand → known defaults)
  • Known firmware bugs (CVE per chipset)
  • Whether WPS is likely vulnerable (Pixie Dust per chipset)
  • Whether KRACK / FragAttacks patches are likely applied (vendor patch cadence)

Data to Record per Target

FieldWhy
BSSIDRequired for every active attack
ESSIDMatch against PNL probes; client probe correlation
Channel + widthPin radio for capture
BandAdapter selection
EncryptionWPA2-PSK / WPA2-Enterprise / WPA3-SAE / Open / WEP
PMF (Protected Management Frames)Whether deauth works
RSSIPosition planning
Beacon interval / TIMAnomaly detection vs. evil-twin defenders
Vendor (OUI)Likely default creds, known bugs
Client list (MACs + RSSI)Targets for deauth/relay
WPS enabled?Pixie Dust candidate

Detection Considerations

A defender's WIDS sees:

  • New device entering the airspace (probe requests reveal even before association)
  • Channel hopping patterns of monitor-mode interfaces
  • Non-standard probe behavior (KARMA-style universal responses, see offensive-evil-twin)

Pure passive recon (no probes from your radio) is invisible to most WIDS deployments. Stay passive until you're committed to the active phase.

Engagement Cheatsheet

bash
# 1. Setup
sudo airmon-ng check kill && sudo airmon-ng start wlan0
sudo iw reg set US
sudo aireplay-ng --test wlan0mon          # confirm injection (skip if pure passive)

# 2. Sweep all bands, write to file
sudo airodump-ng wlan0mon --band abg --write recon --output-format pcap,csv

# 3. Kismet for sustained map (optional)
sudo kismet -c wlan0mon --no-ncurses --daemonize

# 4. Per BSSID drill-down
sudo airodump-ng -c <ch> --bssid <BSSID> -w <name> wlan0mon

# 5. Build target list with all fields above

Key References

Frequently asked questions

What does the Offensive Wifi Recon AI skill do?

Wi-Fi reconnaissance methodology — adapter selection, monitor mode and packet injection setup, regulatory domain handling, multi-band airspace mapping, hidden SSID discovery, BSSID/ESSID/channel/PMF/encryption fingerprinting, client probe analysis, vendor OUI lookup, war-driving with Kismet/airodump-ng/Wigle, and structured airspace data capture for downstream attacks. Use at the start of any wireless engagement to build the target map before active attacks; covers 2.4 GHz, 5 GHz, and 6 GHz (Wi-Fi 6E) bands and adapter compatibility for each.

Why use Offensive Wifi Recon on TypingMind?

Because you install it once and use it with any model. Offensive Wifi Recon 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 Wifi Recon in TypingMind?

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

Which AI models can use Offensive Wifi Recon?

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 Wifi Recon?

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

Is the Offensive Wifi Recon 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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