Automated Subdomain Enumeration
About
This skill automates subdomain discovery using both passive reconnaissance and active techniques like DNS queries and brute-forcing. It's designed for initial reconnaissance to map attack surfaces and identify potentially vulnerable subdomains. The implementation supports multiple languages including bash, python, and go for comprehensive enumeration.
Documentation
Automated Subdomain Enumeration
Overview
Subdomain discovery is a critical first step in reconnaissance. Forgotten subdomains often contain vulnerabilities, outdated software, or misconfigurations that attackers can exploit. A systematic approach combines multiple data sources and techniques to build a comprehensive subdomain list.
Core principle: Combine passive reconnaissance (non-intrusive) with active enumeration (DNS queries, brute-forcing) to maximize coverage while respecting scope and legal boundaries.
When to Use
Use this skill when:
- Starting reconnaissance on a new target domain
- Building an inventory of an organization's attack surface
- Hunting for forgotten or shadow IT assets
- Looking for development, staging, or test environments
- Preparing for comprehensive vulnerability assessment
Don't use when:
- Outside authorized scope (get written permission first)
- Rate limiting or WAF might trigger alerts prematurely
- Target has strict testing windows you must respect
The Multi-Phase Approach
Phase 1: Passive Reconnaissance
Goal: Gather subdomains without directly touching target infrastructure.
Techniques:
-
Certificate Transparency Logs
# Use crt.sh or similar CT log search curl -s "https://crt.sh/?q=%25.target.com&output=json" | \ jq -r '.[].name_value' | \ sed 's/\*\.//g' | \ sort -u > ct_subdomains.txt -
Search Engine Dorking
# Google dorks for subdomains # site:target.com -www # Use tools like subfinder, amass with passive sources subfinder -d target.com -silent -all -o passive_subdomains.txt -
DNS Aggregators
- SecurityTrails
- VirusTotal
- DNSdumpster
- Shodan
-
GitHub/GitLab Code Search
# Search for domain mentions in code # "target.com" site:github.com # Look for: config files, API endpoints, documentation -
Web Archives
# Wayback Machine API curl -s "http://web.archive.org/cdx/search/cdx?url=*.target.com/*&output=json&fl=original&collapse=urlkey" | \ jq -r '.[] | .[0]' | \ grep -oP '(?<=://)[^/]*' | \ sort -u >> archive_subdomains.txt
Phase 2: Active Enumeration
Goal: Actively query DNS infrastructure to discover additional subdomains.
Techniques:
-
Brute Force with Wordlists
# Use tools like puredns, massdns, or dnsx puredns bruteforce wordlist.txt target.com \ --resolvers resolvers.txt \ --write active_subdomains.txt -
DNS Zone Transfers (if misconfigured)
# Test for zone transfer vulnerability dig axfr @ns1.target.com target.com -
Reverse DNS Lookups
# Get IP ranges, perform reverse lookups # Useful for finding additional subdomains on same infrastructure -
Permutation Scanning
# Generate permutations of found subdomains # Example: dev.api.target.com, staging.api.target.com altdns -i subdomains.txt -o permuted.txt -w words.txt dnsx -l permuted.txt -o verified_permuted.txt
Phase 3: Validation and Enrichment
Goal: Verify discovered subdomains are live and gather additional context.
-
Live Host Detection
# Use httpx to probe for web services cat all_subdomains.txt | httpx -silent -o live_hosts.txt # Get status codes, titles, tech stack httpx -l all_subdomains.txt -title -tech-detect -status-code -o enriched.txt -
Port Scanning
# Identify services on discovered hosts naabu -list live_hosts.txt -p 80,443,8080,8443 -o ports.txt -
Screenshot Capture
# Visual reconnaissance of web interfaces gowitness file -f live_hosts.txt -P screenshots/ -
Technology Fingerprinting
# Identify web technologies, frameworks, CMS wappalyzer -l live_hosts.txt -o tech_stack.json
Phase 4: Organization and Analysis
Goal: Structure discovered data for efficient analysis and next steps.
-
Categorize Subdomains
- Production vs. development/staging/test
- Internal-facing vs. external-facing
- By technology stack or function
- By sensitivity/criticality
-
Prioritize Targets
- High-value targets: admin, api, dev, staging, test, vpn, mail
- Outdated software versions
- Unusual ports or services
- Error messages or debug pages
-
Document Findings
# Target: target.com ## Statistics - Total subdomains discovered: X - Live hosts: Y - Unique IP addresses: Z - Technologies identified: [list] ## High-Priority Targets 1. dev.api.target.com - Swagger UI exposed, no auth 2. old-admin.target.com - PHP 5.6, potential RCE 3. test-payment.target.com - Test environment with prod data? ## Next Steps - Deep enumeration of api.target.com endpoints - Vulnerability scan of outdated PHP instance - Manual inspection of test environment
Tool Recommendations
All-in-one suites:
- Amass (comprehensive, integrates many sources)
- Subfinder (fast passive enumeration)
- Assetfinder (simple, effective)
Specialized tools:
- Puredns (DNS validation and brute-forcing)
- DNSx (DNS toolkit with various features)
- Httpx (HTTP probing and enrichment)
- Naabu (fast port scanner)
- Gowitness (screenshot capture)
Setup example:
# Install Go tools
go install github.com/projectdiscovery/subfinder/v2/cmd/subfinder@latest
go install github.com/projectdiscovery/dnsx/cmd/dnsx@latest
go install github.com/projectdiscovery/httpx/cmd/httpx@latest
go install github.com/projectdiscovery/naabu/v2/cmd/naabu@latest
# Install other dependencies
pip install altdns
Automation Script Template
#!/bin/bash
# automated_subdomain_enum.sh
DOMAIN=$1
OUTPUT_DIR="${DOMAIN}_recon_$(date +%Y%m%d_%H%M%S)"
mkdir -p "$OUTPUT_DIR"
echo "[*] Starting subdomain enumeration for $DOMAIN"
# Phase 1: Passive
echo "[*] Phase 1: Passive reconnaissance"
subfinder -d "$DOMAIN" -all -silent -o "$OUTPUT_DIR/subfinder.txt"
assetfinder --subs-only "$DOMAIN" > "$OUTPUT_DIR/assetfinder.txt"
curl -s "https://crt.sh/?q=%25.$DOMAIN&output=json" | jq -r '.[].name_value' | sed 's/\*\.//g' | sort -u > "$OUTPUT_DIR/crtsh.txt"
# Combine and deduplicate
cat "$OUTPUT_DIR"/{subfinder,assetfinder,crtsh}.txt | sort -u > "$OUTPUT_DIR/passive_all.txt"
echo "[+] Found $(wc -l < "$OUTPUT_DIR/passive_all.txt") unique subdomains (passive)"
# Phase 2: Active (optional, comment out if too noisy)
# echo "[*] Phase 2: Active enumeration"
# puredns bruteforce /path/to/wordlist.txt "$DOMAIN" -r /path/to/resolvers.txt -w "$OUTPUT_DIR/bruteforce.txt"
# Phase 3: Validation
echo "[*] Phase 3: Validating and probing subdomains"
cat "$OUTPUT_DIR/passive_all.txt" | dnsx -silent -o "$OUTPUT_DIR/validated.txt"
httpx -l "$OUTPUT_DIR/validated.txt" -title -status-code -tech-detect -silent -o "$OUTPUT_DIR/live_hosts.txt"
echo "[+] Found $(wc -l < "$OUTPUT_DIR/live_hosts.txt") live hosts"
echo "[*] Results saved to $OUTPUT_DIR/"
Legal and Ethical Considerations
CRITICAL: Always follow these rules:
-
Get Written Authorization
- Never test targets without explicit permission
- Scope must be clearly defined in writing
- Understand what techniques are permitted
-
Respect Rate Limits
- Don't overwhelm target DNS servers
- Use reasonable delays between requests
- Consider impact on production systems
-
Handle Data Responsibly
- Discovered subdomains may reveal sensitive information
- Don't publicly disclose findings without permission
- Follow responsible disclosure practices
-
Document Everything
- Keep records of authorization
- Log all activities with timestamps
- Document findings systematically
Common Pitfalls
| Mistake | Impact | Solution |
|---|---|---|
| Only using one data source | Miss many subdomains | Combine multiple techniques |
| Skipping validation | False positives waste time | Always verify with DNS queries |
| Too aggressive active scanning | Detection, blocking, legal issues | Start passive, escalate carefully |
| Not categorizing results | Inefficient analysis | Organize findings by priority |
| Ignoring out-of-scope domains | Legal/ethical violations | Strictly adhere to authorized scope |
Integration with Other Skills
This skill works with:
- skills/reconnaissance/web-app-recon - Next step after finding live web apps
- skills/reconnaissance/service-fingerprinting - Identify services on discovered hosts
- skills/automation/* - Automate the full recon pipeline
- skills/documentation/* - Organize findings in knowledge base
Success Metrics
A successful subdomain enumeration should:
- Discover both obvious and hidden subdomains
- Identify live hosts and their technologies
- Categorize findings by risk/priority
- Provide actionable next steps
- Complete within scope and authorization
- Document findings for future reference
References and Further Reading
- OWASP Testing Guide: Information Gathering
- "Bug Bounty Bootcamp" by Vickie Li (Chapter on Reconnaissance)
- ProjectDiscovery blog on subdomain enumeration
- DNS RFC standards for understanding DNS behavior
Quick Install
/plugin add https://github.com/macaugh/super-rouge-hunter-skills/tree/main/automated-subdomain-enumCopy and paste this command in Claude Code to install this skill
GitHub 仓库
Related Skills
llamaguard
OtherLlamaGuard is Meta's 7-8B parameter model for moderating LLM inputs and outputs across six safety categories like violence and hate speech. It offers 94-95% accuracy and can be deployed using vLLM, Hugging Face, or Amazon SageMaker. Use this skill to easily integrate content filtering and safety guardrails into your AI applications.
sglang
MetaSGLang is a high-performance LLM serving framework that specializes in fast, structured generation for JSON, regex, and agentic workflows using its RadixAttention prefix caching. It delivers significantly faster inference, especially for tasks with repeated prefixes, making it ideal for complex, structured outputs and multi-turn conversations. Choose SGLang over alternatives like vLLM when you need constrained decoding or are building applications with extensive prefix sharing.
langchain
MetaLangChain is a framework for building LLM applications using agents, chains, and RAG pipelines. It supports multiple LLM providers, offers 500+ integrations, and includes features like tool calling and memory management. Use it for rapid prototyping and deploying production systems like chatbots, autonomous agents, and question-answering services.
evaluating-llms-harness
TestingThis Claude Skill runs the lm-evaluation-harness to benchmark LLMs across 60+ standardized academic tasks like MMLU and GSM8K. It's designed for developers to compare model quality, track training progress, or report academic results. The tool supports various backends including HuggingFace and vLLM models.
