implementing-attack-surface-management

What this skill does

# 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

## 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.

```bash
# 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.

```bash
# 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.

```python
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.

```python
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.

```bash
# 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

```python
# Exposure Score = sum of weighted factors, normalized to 0-100
# See agent.py for the full implementation
```

## Examples

```bash
# 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
```