Go 1.21+ for building ProjectDiscovery tools from source
Appropriate authorization for all external scanning activities
Target domains and IP ranges with written scope documentation
Instructions
Phase 1: Subdomain Enumeration with Multiple Sources
Use subfinder for passive subdomain discovery leveraging dozens of data sources
including certificate transparency logs, DNS datasets, and search engines.
# Install ProjectDiscovery tools
go install -v github.com/projectdiscovery/subfinder/v2/cmd/subfinder@latest
go install -v github.com/projectdiscovery/httpx/cmd/httpx@latest
go install -v github.com/projectdiscovery/nuclei/v3/cmd/nuclei@latest
# Basic subdomain enumeration
subfinder -d example.com -o subdomains.txt
# Verbose with all sources and recursive enumeration
subfinder -d example.com -all -recursive -o subdomains_full.txt
# Multi-domain enumeration from file
subfinder -dL domains.txt -o all_subdomains.txt
# Using OWASP Amass for deeper enumeration
amass enum -d example.com -passive -o amass_subdomains.txt
# Merge and deduplicate results
cat subdomains.txt amass_subdomains.txt | sort -u > combined_subdomains.txt
Phase 2: Live Host Discovery and Service Fingerprinting
Probe discovered subdomains to identify live hosts, technologies, and services.
Query Shodan for exposed services, open ports, and known vulnerabilities
associated with discovered assets.
import shodan
api = shodan.Shodan("YOUR_SHODAN_API_KEY")
# Search by organization
results = api.search("org:\"Example Corp\"")
for service in results["matches"]:
print(f"{service['ip_str']}:{service['port']} - {service.get('product', 'unknown')}")
if service.get("vulns"):
for cve in service["vulns"]:
print(f" CVE: {cve}")
# Search by hostname
results = api.search("hostname:example.com")
# Search by SSL certificate
results = api.search("ssl.cert.subject.cn:example.com")
# Get host details with all services
host = api.host("93.184.216.34")
print(f"IP: {host['ip_str']}")
print(f"Ports: {host['ports']}")
print(f"Vulns: {host.get('vulns', [])}")
Phase 4: Censys Asset Discovery
Use Censys to discover internet-facing assets through certificate and host search.
from censys.search import CensysHosts, CensysCerts
# Host search
hosts = CensysHosts()
query = hosts.search("services.tls.certificates.leaf.subject.common_name: example.com")
for page in query:
for host in page:
print(f"IP: {host['ip']}")
for service in host.get("services", []):
print(f" Port: {service['port']} Protocol: {service['transport_protocol']}")
print(f" Service: {service.get('service_name', 'unknown')}")
# Certificate transparency search
certs = CensysCerts()
query = certs.search("parsed.names: example.com")
for page in query:
for cert in page:
print(f"Fingerprint: {cert['fingerprint_sha256']}")
print(f"Names: {cert.get('parsed', {}).get('names', [])}")
Phase 5: Vulnerability Scanning with Nuclei
Run targeted vulnerability scans against discovered assets using Nuclei templates.
# Update nuclei templates
nuclei -ut
# Scan with all templates
cat combined_subdomains.txt | httpx -silent | nuclei -o nuclei_results.txt
# Scan with specific severity
cat combined_subdomains.txt | httpx -silent | \
nuclei -severity critical,high -o critical_findings.txt
# Scan with specific template categories
cat combined_subdomains.txt | httpx -silent | \
nuclei -tags cve,misconfig,exposure -o categorized_findings.txt
# Scan for exposed panels and sensitive files
cat combined_subdomains.txt | httpx -silent | \
nuclei -tags panel,exposure,config -o exposed_panels.txt
Phase 6: Exposure Scoring Algorithm
Score each asset based on OWASP attack surface analysis principles, using
a weighted formula derived from the Relative Attack Surface Quotient (RSQ)
and damage-potential-to-effort ratio.
The scoring algorithm considers:
Open ports and services - weighted by service risk (management ports score higher)
Known vulnerabilities - weighted by CVSS score
Technology age - outdated software increases score
Exposure level - internet-facing vs. authenticated access
Data sensitivity - based on service type and content indicators
# Exposure Score = sum of weighted factors, normalized to 0-100
# See agent.py for the full implementation