Passive Recon

Passive Reconnaissance Knowledge Base

Passive reconnaissance gathers intelligence without directly interacting with the target's systems. This leaves no logs, no alerts, and no fingerprints on the target. Always exhaust passive methods before transitioning to active techniques.

Quick Reference — Copy-Paste Commands

# Full passive workflow for <TARGET>
whois <TARGET>
dig <TARGET> ANY +noall +answer
subfinder -d <TARGET> -o subdomains.txt
amass enum -passive -d <TARGET> -o amass_subs.txt
curl -s "https://crt.sh/?q=%25.<TARGET>&output=json" | python3 -c "import sys,json; [print(x['name_value']) for x in json.load(sys.stdin)]" | sort -u
httpx -l subdomains.txt -sc -cl -ct -title -tech-detect -o httpx_<TARGET>.txt
curl -sI https://<TARGET>

1. WHOIS Intelligence

Domain WHOIS

whois example.com

Extract: Registrar, creation/expiration dates, nameservers, registrant organization, abuse contacts.

IP WHOIS (ASN Mapping)

whois -h whois.radb.net -- '-i origin AS12345'
whois <IP_ADDRESS>

Extract: ASN ownership, IP ranges allocated, network name, organization.

BGP/ASN Enumeration

# ASN lookup via Team Cymru
whois -h whois.cymru.com " -v <IP_ADDRESS>"

# All prefixes for an ASN
whois -h whois.radb.net -- '-i origin AS12345' | grep route

# Using amass for ASN intelligence
amass intel -asn <ASN_NUMBER>

Key Analysis Points

• Registrar patterns: Same registrar across multiple domains may indicate shared ownership
• Nameserver clustering: Shared NS records reveal infrastructure relationships
• Registration dates: Recently registered domains may indicate campaign infrastructure
• Privacy protection: WHOIS privacy services indicate security awareness
• ASN ownership: Map all IP ranges belonging to the target organization

2. DNS Reconnaissance

Comprehensive Record Queries

# All record types
dig example.com ANY +noall +answer

# Specific records
dig example.com A +short
dig example.com AAAA +short
dig example.com MX +short
dig example.com NS +short
dig example.com TXT +short
dig example.com CNAME +short
dig example.com SOA +short

# Reverse DNS
dig -x <IP_ADDRESS> +short

DNS Zone Transfer Attempt

# Enumerate nameservers first
dig example.com NS +short

# Attempt zone transfer (AXFR)
dig @ns1.example.com example.com AXFR

Note: Zone transfers are a grey area — they are a DNS protocol feature but unauthorized transfers may violate ROE. Confirm scope before attempting.

DNS Analysis Patterns

• MX records: Reveal email infrastructure (Google Workspace, Microsoft 365, self-hosted)
• TXT records: SPF, DKIM, DMARC reveal email security posture; may contain verification tokens leaking service usage
• NS delegation: Identifies DNS hosting provider (Cloudflare, AWS Route53, etc.)
• CNAME chains: Expose CDN usage, third-party service integrations, potential subdomain takeover candidates
• SOA records: Reveal primary DNS admin, serial numbers (change frequency indicator)
• CAA records: Certificate Authority Authorization — reveals which CAs can issue certs

3. Subdomain Enumeration

subfinder (Primary Tool)

# Basic enumeration
subfinder -d example.com -silent

# Save to file for large results
subfinder -d example.com -o subdomains.txt

# Multiple sources with verbose
subfinder -d example.com -all -v

# Recursive enumeration
subfinder -d example.com -recursive

amass (Comprehensive)

# Passive-only enumeration (no DNS brute force)
amass enum -passive -d example.com -o amass_subs.txt

# With additional intelligence sources
amass enum -passive -d example.com -src -ip

# Intel mode — discover root domains from ASN/org
amass intel -org "Target Corp"
amass intel -asn 12345 -whois -d example.com

DNS Brute Force (Secondary)

# Using a wordlist if available
for sub in $(cat /usr/share/wordlists/subdomains.txt); do
    dig +short "$sub.example.com" | grep -v "^$" && echo "$sub.example.com"
done

Subdomain Analysis

• Naming patterns: dev., staging., test., admin., vpn., mail. reveal internal structure
• IP clustering: Group subdomains by IP to identify shared hosting / infrastructure segments
• Cloud indicators: *.amazonaws.com, *.azurewebsites.net CNAME targets reveal cloud usage
• Dangling CNAMEs: Subdomains pointing to deprovisioned services = potential subdomain takeover

4. Certificate Transparency (CT) Logs

Using crt.sh

curl -s "https://crt.sh/?q=%25.example.com&output=json" | \
    python3 -c "import sys,json; [print(x['name_value']) for x in json.load(sys.stdin)]" | \
    sort -u

Analysis Value

• CT logs are a public, immutable record of all TLS certificates issued
• Reveals subdomains not found by DNS enumeration
• Shows historical certificates — even revoked ones expose past infrastructure
• Wildcard certificates (*.example.com) indicate broad subdomain usage

5. Web Fingerprinting (Passive)

HTTP Headers Analysis

curl -sI https://example.com

Look for:

• Server: Web server software and version
• X-Powered-By: Backend framework
• X-CDN, CF-RAY: CDN identification
• Strict-Transport-Security: HSTS configuration
• Content-Security-Policy: CSP reveals allowed domains / integrations

httpx — Bulk Probing & Tech Detection

# Probe all subdomains with tech detection
httpx -l subdomains.txt -sc -cl -ct -title -tech-detect -o httpx_results.txt

# Filter live hosts with specific status codes
httpx -l subdomains.txt -mc 200,301,302,403 -title -tech-detect

# JSON output for parsing
httpx -l subdomains.txt -sc -title -tech-detect -json -o httpx.json

httpx is critical for:

• Validating which subdomains are actually alive (HTTP response)
• Bulk technology fingerprinting (frameworks, CDN, WAF)
• Status code and content-length triage

Technology Stack Detection

curl -s https://example.com | grep -Ei '(wp-content|drupal|joomla|next|react|angular|vue)'

6. Tool Chaining & Output Normalization

Piping Between Tools

# subfinder → httpx → nuclei pipeline
subfinder -d <TARGET> -silent | httpx -silent -sc -title -tech-detect | tee live_<TARGET>.txt
cat live_<TARGET>.txt | awk '{print $1}' | nuclei -severity critical,high -silent

# CT logs → dedup → resolve
curl -s "https://crt.sh/?q=%25.<TARGET>&output=json" | \
  python3 -c "import sys,json; [print(x['name_value']) for x in json.load(sys.stdin)]" | \
  sort -u | httpx -silent -o ct_live_<TARGET>.txt

# amass + subfinder → merge → dedup
cat amass_subs.txt subdomains.txt | sort -u > all_subs_<TARGET>.txt

Output Normalization

Normalize all subdomain sources into a single format for downstream tools:

# Standard format: one subdomain per line, no protocol, no trailing dot
cat all_subs_<TARGET>.txt | \
  sed 's|https\?://||; s|/.*||; s|\.$||' | \
  tr '[:upper:]' '[:lower:]' | sort -u > normalized_subs_<TARGET>.txt

7. Passive DNS & Historical Analysis

Passive DNS Sources

# SecurityTrails API (if available)
curl -s "https://api.securitytrails.com/v1/domain/<TARGET>/subdomains" \
  -H "APIKEY: $ST_KEY" | python3 -m json.tool

# VirusTotal passive DNS
curl -s "https://www.virustotal.com/api/v3/domains/<TARGET>/resolutions?limit=40" \
  -H "x-apikey: $VT_KEY" | python3 -c "
import sys, json
data = json.load(sys.stdin)
for r in data.get('data', []):
  attrs = r['attributes']
  print(f\"{attrs.get('date','?')}  {attrs.get('host_name','?')} -> {attrs.get('ip_address','?')}\")
"

Historical DNS Value

• IP changes reveal infrastructure migrations (on-prem → cloud)
• Old A records may still resolve to accessible legacy servers
• Expired CNAME targets are subdomain takeover candidates
• MX history shows email provider changes

8. OSINT Handoff

Boundary: Passive recon covers technical infrastructure (DNS, subdomains, WHOIS, ASN, CT logs, web fingerprinting). For human and organizational intelligence (email harvesting, employee enumeration, GitHub secret scanning, breach data, social media, Google dorking, Wayback Machine), use the dedicated osint skill.

After completing passive recon, hand off discovered domains and infrastructure data to the osint skill for:

• Email pattern discovery from discovered domains
• Employee and org structure enumeration
• GitHub/GitLab secret scanning for discovered organizations
• Breach exposure assessment for discovered email domains
• Google dorking using discovered subdomains and paths
• Wayback Machine analysis of discovered URLs

9. Error Handling & Edge Cases

When Tools Fail

False Positive Reduction

• Wildcard DNS: Check if *.TARGET resolves → if yes, filter out wildcard IPs from subdomain results
• Parking pages: httpx title containing "parked", "coming soon", "default" → deprioritize
• CDN IPs: Multiple subdomains resolving to same CDN IP ≠ same server

# Detect wildcard DNS
WILDCARD_IP=$(dig +short nonexistent-random-string.<TARGET>)
if [ -n "$WILDCARD_IP" ]; then
  echo "WILDCARD detected: $WILDCARD_IP — filtering results"
  grep -v "$WILDCARD_IP" resolved_subs.txt > filtered_subs.txt
fi

10. Workflow: Passive Recon Sequence

1. WHOIS + ASN → Establish ownership, registrar, nameservers, IP ranges
2. DNS Records → Map A, MX, NS, TXT, SOA, CAA records
3. Subdomain Enumeration → subfinder + amass + CT logs
4. Deduplicate & Normalize → Merge all sources, remove wildcards
5. Live Host Probing → httpx on all discovered subdomains
6. Passive DNS History → Check for old IPs, infrastructure changes
7. Web Fingerprinting → Technology stack on key assets
8. → Hand off to osint skill → Email, employee, GitHub, breach, social media, dorking
9. Synthesis → Build infrastructure map, identify high-value targets for active phase

11. Decision Gate: Passive → Active Transition

Before moving to active reconnaissance, you must have:

• Complete domain/subdomain inventory (deduplicated, wildcard-filtered)
• DNS infrastructure map (nameservers, mail servers, CDN)
• IP address grouping and ASN mapping
• Live host validation (httpx results)
• Technology stack indicators
• Passive DNS history reviewed
• OSINT findings documented
• Identified high-value targets that justify active probing

Bundled Resources

References

• references/dns-techniques.md — DNS record types, subdomain tool comparison, CT deep dive, ASN/BGP intel, passive DNS databases. Read when you need detailed technique reference beyond this skill's quick-reference commands.

Scripts

• scripts/parse_subdomains.py — Parse and deduplicate subdomain results from multiple tools. Usage: python scripts/parse_subdomains.py recon/*.txt -d <TARGET> -o recon/all_subs.txt