Implementing Attack Surface Management

Implementing Attack Surface Management

When to Use

• When building an external attack surface management (EASM) program from scratch
• When performing authorized external reconnaissance for penetration testing engagements
• When continuously monitoring organizational exposure across internet-facing assets
• When scoring and prioritizing external attack surface risks for remediation
• When integrating multiple discovery tools into an automated ASM pipeline

Most Often Missed & How to Confirm

• Acquisition/sibling assets: teams scan the apex domain but miss subsidiaries, fresh ASNs, and recently registered domains. Confirm ownership via amass intel -asn/-org, WHOIS registrant correlation, and matching TLS cert organization fields before adding to scope.
• Cert-transparency-only hosts with no DNS: a name in a CT log isn't live. Confirm with dnsx resolution then httpx -sc -title — only record assets that actually answer.
• Shodan/Censys staleness: banners can be days/weeks old. Confirm an exposure is current by directly re-probing the ip:port (httpx, nmap -sV) before scoring it; flag closed/changed services as historical.
• Non-HTTP exposure: databases (3306/5432/27017/6379), RDP/SMB, Kubernetes API (6443/10250), and message brokers get skipped by HTTP-only pipelines. Confirm with a targeted nmap -sV and an unauthenticated connection test.
• Nuclei false positives: a single template hit is a lead, not a finding. Confirm by manually reproducing the request/response, checking the matched string is genuinely the vuln (not an error page), and re-running to rule out flapping.
• CVE inference from version banners: a version string suggests, it doesn't prove. Confirm exploitability against the live service or note it explicitly as "version-implied, unverified."
• Scope drift: shared CDN/SaaS IPs (Cloudflare, AWS, GitHub Pages) frequently fall outside authorization even when the hostname matches — confirm the underlying IP/org sits in the signed scope before any active scan.

Prerequisites

• Python 3.8+ with requests, shodan, censys libraries installed
• Shodan API key (free tier provides 100 queries/month)
• Censys API ID and Secret (free tier available)
• ProjectDiscovery tools installed: subfinder, httpx, nuclei
• 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.

# HTTP probing with technology detection
cat combined_subdomains.txt | httpx -sc -cl -ct -title -tech-detect \
    -follow-redirects -json -o httpx_results.json

# Detailed service fingerprinting
cat combined_subdomains.txt | httpx -sc -cl -ct -title -tech-detect \
    -favicon -hash sha256 -jarm -cdn -cname \
    -follow-redirects -json -o httpx_detailed.json

Phase 3: Shodan Asset Discovery

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:

1. Open ports and services - weighted by service risk (management ports score higher)
2. Known vulnerabilities - weighted by CVSS score
3. Technology age - outdated software increases score
4. Exposure level - internet-facing vs. authenticated access
5. 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

Examples

# Run complete ASM pipeline against a target domain
python agent.py \
    --domain example.com \
    --action full_scan \
    --shodan-key YOUR_KEY \
    --censys-id YOUR_ID \
    --censys-secret YOUR_SECRET \
    --output asm_report.json

# Subdomain enumeration only
python agent.py \
    --domain example.com \
    --action enumerate \
    --output subdomains.json

# Exposure scoring on previously discovered assets
python agent.py \
    --domain example.com \
    --action score \
    --input previous_scan.json \
    --output scored_assets.json

# Multi-domain scan from file
python agent.py \
    --domain-list targets.txt \
    --action full_scan \
    --output multi_domain_report.json