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Vhost Enumeration

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uphiago
vhost-enumeration

Discover hidden virtual hosts via Host header fuzzing and SSL certificate parsing.

Overview

Publisheruphiago
Repositoryrecon-skills
Skill namevhost-enumeration
Stars
1.3K
Forks
213
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 uphiago on GitHub. Read the source before you install it.

Installation

Install the Vhost Enumeration 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.
  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/uphiago/recon-skills.git /tmp/recon-skills
mkdir -p .claude/skills
cp -r /tmp/recon-skills/recon/vhost-enumeration .claude/skills/vhost-enumeration
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Vhost Enumeration 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 Vhost Enumeration 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 Vhost Enumeration 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.

Virtual Host Enumeration

Discover hidden virtual hosts on IP addresses by fuzzing the Host header. Many servers only respond to specific domain names and remain invisible to standard subdomain enumeration. VHOST fuzzing exposes internal services, development environments, and admin panels that share the same IP but answer to different hostnames.

When to Use

  • You have a list of target IPs from skill_view(name='origin-ip-discovery') or skill_view(name='port-service-discovery').
  • A server returns default/blank pages for unknown Host headers.
  • Subdomain enumeration may have missed internal-only hostnames.
  • SSL certificates on an IP list multiple domain names in the SAN field.
  • You need to map internal services behind a reverse proxy.

Prerequisites

  • terminal with curl, ffuf, dnsx, and httpx.
  • A DNS wordlist for hostname fuzzing.
  • A list of target IP addresses.

Quick Detection

bash
# Basic VHOST fuzz on a single IP
ffuf -u http://TARGET_IP \
  -w /path/to/wordlist.txt \
  -H "Host: FUZZ.target.com" \
  -fs 0 -mc 200,301,302,401,403

Procedure

Phase 1 — Host Header Fuzzing

bash
# Fuzz for virtual hosts matching the target domain pattern
ffuf -u http://TARGET_IP \
  -w $DNS_WORDLIST \
  -H "Host: FUZZ.target.com" \
  -fs DEFAULT_RESPONSE_SIZE \
  -mc 200,301,302,401,403 \
  -o vhost_ffuf.json

# HTTPS variant
ffuf -u https://target.com \
  -w $DNS_WORDLIST \
  -H "Host: FUZZ.target.com" \
  -mc 200,301,302,401,403

# Fuzz multiple IPs with a wordlist
cat unique_ips.txt | while read ip; do
  ffuf -u "http://$ip" \
    -w $DNS_WORDLIST \
    -H "Host: FUZZ.target.com" \
    -fs 0 -mc 200,301,302 -o "vhost_$ip.json"
  sleep 0.5
done

Phase 2 — Response Filtering

bash
# Auto-calibrate: ffuf detects default response size and filters it out
ffuf -u http://TARGET_IP \
  -w $DNS_WORDLIST \
  -H "Host: FUZZ.target.com" \
  -ac -sf -s \
  -mc 200

# Manual calibration: find the default response size first
curl --max-time 30 --connect-timeout 10 -s http://TARGET_IP -H "Host: nonexistentxxxxx12345.target.com" | wc -c
# Use that size as -fs filter

Phase 3 — Accessing VHOST Targets

bash
# Method 1 — direct curl with Host header
curl --max-time 30 --connect-timeout 10 -H "Host: dev.target.com" http://TARGET_IP

# Method 2 — /etc/hosts injection for browser access
echo "TARGET_IP  dev.target.com internal.target.com admin.target.com" | sudo tee -a /etc/hosts
# Then open http://dev.target.com in browser

# Method 3 — httpx with custom host resolution
echo "http://dev.target.com" | httpx -silent

Phase 4 — SSL Certificate SAN Enumeration

bash
# Extract hostnames from SSL certificate
echo | openssl s_client -connect TARGET_IP:443 -servername target.com 2>/dev/null \
  | openssl x509 -noout -text \
  | grep -Eo 'DNS:[^,\s]+' | cut -d: -f2 | sort -u

# Batch: extract hostnames from all discovered IPs
cat unique_ips.txt | while read ip; do
  echo | timeout 5 openssl s_client -connect $ip:443 2>/dev/null \
    | openssl x509 -noout -text 2>/dev/null \
    | grep -Eo 'DNS:[^,\s]+' | cut -d: -f2 | sort -u >> ssl_hostnames.txt
  sleep 0.5
done

# Check which of those hostnames resolve to the target
cat ssl_hostnames.txt | dnsx -silent -a -resp-only | sort -u

Phase 5 — PTR Reverse DNS on IP Ranges

bash
# If you have IP ranges, resolve PTR records to find hostnames
echo "10.0.0.0/23" | dnsx -silent -resp-only -ptr

# Batch on multiple CIDR ranges
cat cidr_ranges.txt | mapcidr -silent | dnsx -ptr -resp-only -silent > ptr_domains.txt

Phase 6 — Content Differencing

bash
# For each found VHOST, take a screenshot for visual triage
cat found_vhosts.txt | gowitness file -f - --no-http -P ./vhost_screenshots/

# Compare content across hosts — different content = different service
for host in $(cat found_vhosts.txt); do
  curl --max-time 30 --connect-timeout 10 -sk "http://TARGET_IP" -H "Host: $host" | md5sum
  sleep 0.3
done | sort

Pitfalls

  • Wildcard DNS returns valid HTTP for any Host header. Use auto-calibration (-ac) and verify manually.
  • The default virtual host may return a generic page for unknown names. Calibrate -fs with a known-nonexistent hostname.
  • SSL/TLS prevents content comparison without SNI. Use openssl s_client -servername for each hostname.
  • Some servers accept any Host header. This produces false positives — verify each finding with manual curl.
  • Internal-only services may not be accessible from your IP. They may require internal network access.

Verification

  1. ffuf returns a status code and response size different from the calibrated default.
  2. Manual curl with the discovered Host header returns meaningful content (not the default page).
  3. The discovered hostname is not in the public subdomain list (previously unknown).
  4. SSL certificate SAN field confirms the hostname belongs on this server.
  5. Take a screenshot and confirm it's a distinct application or service.

Related Skills

  • subdomain-enumeration — Generate the DNS wordlist and existing subdomains.
  • origin-ip-discovery — Find the origin IPs that need VHOST scanning.
  • web-enumeration — Once VHOSTs are found, fuzz directories and endpoints.

Frequently asked questions

What does the Vhost Enumeration AI skill do?

Discover hidden virtual hosts via Host header fuzzing and SSL certificate parsing.

Why use Vhost Enumeration on TypingMind?

Because you install it once and use it with any model. Vhost Enumeration 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 Vhost Enumeration in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/uphiago/recon-skills/tree/main/recon/vhost-enumeration. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Vhost Enumeration?

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 Vhost Enumeration?

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

Is the Vhost Enumeration AI skill free?

Yes. It is published on GitHub by uphiago 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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