Analyzing Certificate Transparency For Phishing

---
name: analyzing-certificate-transparency-for-phishing
description: Use when monitor Certificate Transparency logs using crt.sh and Certstream
  to detect phishing domains, lookalike certificates, and unauthorized certificate
  issuance targeting your organization. Use when monitoring certificate transparency
  logs using crt.sh and certstream to detect.
domain: cybersecurity
subdomain: threat-intelligence
tags:
- certificate-transparency
- ct-logs
- phishing
- crt-sh
- certstream
- ssl
- domain-monitoring
- threat-intelligence
version: '1.0'
author: oyi77
license: Apache-2.0
atlas_techniques:
- AML.T0052
nist_csf:
- ID.RA-01
- ID.RA-05
- DE.CM-01
- DE.AE-02
category: cybersecurity
---

# Analyzing Certificate Transparency for Phishing

## Overview

Certificate Transparency (CT) is an Internet security standard that creates a public, append-only log of all issued SSL/TLS certificates. Monitoring CT logs enables early detection of phishing domains that register certificates mimicking legitimate brands, unauthorized certificate issuance for owned domains, and certificate-based attack infrastructure. This skill covers querying CT logs via crt.sh, real-time monitoring with Certstream, building automated alerting for suspicious certificates, and integrating findings into threat intelligence workflows.


## When to Use
**Trigger phrases:**
- "analyzing certificate transparency for phishing"
- "Monitor Certificate Transparency logs using crt"


- When investigating security incidents that require analyzing certificate transparency for phishing
- When building detection rules or threat hunting queries for this domain
- When SOC analysts need structured procedures for this analysis type
- When validating security monitoring coverage for related attack techniques

## Prerequisites

- Python 3.9+ with `requests`, `certstream`, `tldextract`, `Levenshtein` libraries
- Access to crt.sh (https://crt.sh/) for historical CT log queries
- Certstream (https://certstream.calidog.io/) for real-time monitoring
- List of organization domains and brand keywords to monitor
- Understanding of SSL/TLS certificate structure and issuance process

## Key Concepts

This section covers key concepts for analyzing certificate transparency for phishing.

- Ensure all prerequisites are met before proceeding
- Follow the documented workflow steps in sequence
- Record results and any anomalies encountered during this phase
### Certificate Transparency Logs

CT logs are cryptographically assured, publicly auditable, append-only records of TLS certificate issuance. Major CAs (Let's Encrypt, DigiCert, Sectigo, Google Trust Services) submit all issued certificates to multiple CT logs. As of 2025, Chrome and Safari require CT for all publicly trusted certificates.

### Phishing Detection via CT

Attackers register lookalike domains and obtain free certificates (often from Let's Encrypt) to make phishing sites appear legitimate with HTTPS. CT monitoring detects these early because the certificate appears in logs before the phishing campaign launches, providing a window for proactive blocking.

### crt.sh Database

crt.sh is a free web interface and PostgreSQL database operated by Sectigo that indexes CT logs. It supports wildcard searches (`%.example.com`), direct SQL queries, and JSON API responses. It tracks certificate issuance, expiration, and revocation across all major CT logs.

## Workflow

1. **Scope the task** — define objectives, boundaries, and success criteria
2. **Gather information** — collect all necessary data and context before proceeding
3. **Execute the core workflow** — follow the domain-specific steps methodically
4. **Validate results** — verify outputs against expected outcomes or baselines
5. **Document findings** — record results, anomalies, and recommendations
### Step 1: Query crt.sh for Certificate History

```python
import requests
import json
from datetime import datetime
import tldextract

class CTLogMonitor:
    CRT_SH_URL = "https://crt.sh"

    def __init__(self, monitored_domains, brand_keywords):
        self.monitored_domains = monitored_domains
        self.brand_keywords = [k.lower() for k in brand_keywords]

    def query_crt_sh(self, domain, include_expired=False):
        """Query crt.sh for certificates matching a domain."""
        params = {
            "q": f"%.{domain}",
            "output": "json",
        }
        if not include_expired:
            params["exclude"] = "expired"

        resp = requests.get(self.CRT_SH_URL, params=params, timeout=30)
        if resp.status_code == 200:
            certs = resp.json()
            print(f"[+] crt.sh: {len(certs)} certificates for *.{domain}")
            return certs
        return []

    def find_suspicious_certs(self, domain):
        """Find certificates that may be phishing attempts."""
        certs = self.query_crt_sh(domain)
        suspicious = []

        for cert in certs:
            common_name = cert.get("common_name", "").lower()
            name_value = cert.get("name_value", "").lower()
            issuer = cert.get("issuer_name", "")
            not_before = cert.get("not_before", "")
            not_after = cert.get("not_after", "")

            # Check for exact domain matches (legitimate)
            extracted = tldextract.extract(common_name)
            cert_domain = f"{extracted.domain}.{extracted.suffix}"
            if cert_domain == domain:
                continue  # Legitimate certificate

            # Flag suspicious patterns
            flags = []
            if domain.replace(".", "") in common_name.replace(".", ""):
                flags.append("contains target domain string")
            if any(kw in common_name for kw in self.brand_keywords):
                flags.append("contains brand keyword")
            if "let's encrypt" in issuer.lower():
                flags.append("free CA (Let's Encrypt)")

            if flags:
                suspicious.append({
                    "common_name": cert.get("common_name", ""),
                    "name_value": cert.get("name_value", ""),
                    "issuer": issuer,
                    "not_before": not_before,
                    "not_after": not_after,
                    "serial": cert.get("serial_number", ""),
                    "flags": flags,
                    "crt_sh_id": cert.get("id", ""),
                    "crt_sh_url": f"https://crt.sh/?id={cert.get('id', '')}",
                })

        print(f"[+] Found {len(suspicious)} suspicious certificates")
        return suspicious

monitor = CTLogMonitor(
    monitored_domains=["mycompany.com", "mycompany.org"],
    brand_keywords=["mycompany", "mybrand", "myproduct"],
)
suspicious = monitor.find_suspicious_certs("mycompany.com")
for cert in suspicious[:5]:
    print(f"  [{cert['common_name']}] Flags: {cert['flags']}")
```

### Step 2: Real-Time Monitoring with Certstream

```python
import certstream
import Levenshtein
import re
from datetime import datetime

class CertstreamMonitor:
    def __init__(self, watched_domains, brand_keywords, similarity_threshold=0.8):
        self.watched_domains = [d.lower() for d in watched_domains]
        self.brand_keywords = [k.lower() for k in brand_keywords]
        self.threshold = similarity_threshold
        self.alerts = []

    def start_monitoring(self, max_alerts=100):
        """Start real-time CT log monitoring."""
        print("[*] Starting Certstream monitoring...")
        print(f"    Watching: {self.watched_domains}")
        print(f"    Keywords: {self.brand_keywords}")

        def callback(message, context):
            if message["message_type"] == "certificate_update":
                data = message["data"]
                leaf = data.get("leaf_cert", {})
                all_domains = leaf.get("all_domains", [])

                for domain in all_domains:
                    domain_lower = domain.lower().strip("*.")
                    if self._is_suspicious(domain_lower):
                        alert = {
                            "domain": domain,
                            "all_domains": all_domains,
                            "issuer": leaf.get("issuer", {}).get("O", ""),
                            "fingerprint": leaf.get("fingerprint", ""),
                            "not_before": leaf.get("not_before", ""),
                            "detected_at": datetime.now().isoformat(),
                            "reason": self._get_reason(domain_lower),
                        }
                        self.alerts.append(alert)
                        print(f"  [ALERT] {domain} - {alert['reason']}")

                        if len(self.alerts) >= max_alerts:
                            raise KeyboardInterrupt

        try:
            certstream.listen_for_events(callback, url="wss://certstream.calidog.io/")
        except KeyboardInterrupt:
            print(f"\n[+] Monitoring stopped. {len(self.alerts)} alerts collected.")
        return self.alerts

    def _is_suspicious(self, domain):
        """Check if domain is suspicious relative to watched domains."""
        for watched in self.watched_domains:
            # Exact keyword match
            watched_base = watched.split(".")[0]
            if watched_base in domain and domain != watched:
                return True

            # Levenshtein distance (typosquatting detection)
            domain_base = tldextract.extract(domain).domain
            similarity = Levenshtein.ratio(watched_base, domain_base)
            if similarity >= self.threshold and domain_base != watched_base:
                return True

        # Brand keyword match
        for keyword in self.brand_keywords:
            if keyword in domain:
                return True

        return False

    def _get_reason(self, domain):
        """Determine why domain was flagged."""
        reasons = []
        for watched in self.watched_domains:
            watched_base = watched.split(".")[0]
            if watched_base in domain:
                reasons.append(f"contains '{watched_base}'")
            domain_base = tldextract.extract(domain).domain
            similarity = Levenshtein.ratio(watched_base, domain_base)
            if similarity >= self.threshold and domain_base != watched_base:
                reasons.append(f"similar to '{watched}' ({similarity:.0%})")
        for kw in self.brand_keywords:
            if kw in domain:
                reasons.append(f"brand keyword '{kw}'")
        return "; ".join(reasons) if reasons else "unknown"

cs_monitor = CertstreamMonitor(
    watched_domains=["mycompany.com"],
    brand_keywords=["mycompany", "mybrand"],
    similarity_threshold=0.75,
)
alerts = cs_monitor.start_monitoring(max_alerts=50)
```

### Step 3: Enumerate Subdomains from CT Logs

```python
def enumerate_subdomains_ct(domain):
    """Discover all subdomains from Certificate Transparency logs."""
    params = {"q": f"%.{domain}", "output": "json"}
    resp = requests.get("https://crt.sh", params=params, timeout=30)

    if resp.status_code != 200:
        return []

    certs = resp.json()
    subdomains = set()
    for cert in certs:
        name_value = cert.get("name_value", "")
        for name in name_value.split("\n"):
            name = name.strip().lower()
            if name.endswith(f".{domain}") or name == domain:
                name = name.lstrip("*.")
                subdomains.add(name)

    sorted_subs = sorted(subdomains)
    print(f"[+] CT subdomain enumeration for {domain}: {len(sorted_subs)} subdomains")
    return sorted_subs

subdomains = enumerate_subdomains_ct("example.com")
for sub in subdomains[:20]:
    print(f"  {sub}")
```

### Step 4: Generate CT Intelligence Report

```
def generate_ct_report(suspicious_certs, certstream_alerts, domain):
    report = f"""# Certificate Transparency Intelligence Report
## Target Domain: {domain}
This section covers target domain: {domain} for analyzing certificate transparency for phishing.

- Ensure all prerequisites are met before proceeding
- Follow the documented workflow steps in sequence
- Record results and any anomalies encountered during this phase
## Generated: {datetime.now().isoformat()}

```
Analyzing Certificate Transparency For Phishing Output Summary
========================================
Status:       [COMPLETE / PARTIAL / BLOCKED]
Findings:     [count] items
Severity:     [Critical / High / Medium / Low / Info]
Evidence:     [file paths or log references]
Next Steps:   [recommended actions]
```
## Summary
- Suspicious certificates found: {len(suspicious_certs)}
- Real-time alerts triggered: {len(certstream_alerts)}

## Suspicious Certificates (crt.sh)
| Common Name | Issuer | Flags | crt.sh Link |
|------------|--------|-------|-------------|
"""
    for cert in suspicious_certs[:20]:
        flags = "; ".join(cert.get("flags", []))
        report += (f"| {cert['common_name']} | {cert['issuer'][:30]} "
                   f"| {flags} | [View]({cert['crt_sh_url']}) |\n")

    report += f"""
## Real-Time Certstream Alerts
| Domain | Issuer | Reason | Detected |
|--------|--------|--------|----------|
"""
    for alert in certstream_alerts[:20]:
        report += (f"| {alert['domain']} | {alert['issuer']} "
                   f"| {alert['reason']} | {alert['detected_at'][:19]} |\n")

    report += """
## Recommendations
1. Add flagged domains to DNS sinkhole / web proxy blocklist
2. Submit takedown requests for confirmed phishing domains
3. Monitor CT logs continuously for new certificate registrations
4. Implement CAA DNS records to restrict certificate issuance for your domains
5. Deploy DMARC to prevent email spoofing from lookalike domains
"""
    with open(f"ct_report_{domain.replace('.','_')}.md", "w") as f:
        f.write(report)
    print(f"[+] CT report saved")
    return report

generate_ct_report(suspicious, alerts if 'alerts' in dir() else [], "mycompany.com")
```

## Validation Criteria

- crt.sh queries return certificate data for target domains
- Suspicious certificates identified based on lookalike patterns
- Certstream real-time monitoring detects new phishing certificates
- Subdomain enumeration produces comprehensive list from CT logs
- Alerts generated with reason classification
- CT intelligence report created with actionable recommendations

## When NOT to Use

- You need to perform the attack, not analyze it (use performing-* skills)
- Task is about detection, not analysis (use detecting-* skills)
- You need to implement controls (use implementing-* skills)
- Task is about threat hunting, not post-incident analysis (use hunting-* skills)
- You don't have access to the artifacts/logs to analyze
- Task requires real-time monitoring (use SOC tools)


## Red Flags

- Performing actions without explicit written authorization from the asset owner
- Testing against production systems without a defined scope and rules of engagement
- Sharing sensitive findings or credentials in unencrypted communications
- Failing to properly scope and contain the assessment before starting

## Verification

- All steps executed successfully against a test environment before production use
- Output documented with screenshots or logs demonstrating expected behavior
- Results validated against known-good baselines or reference implementations
- Documentation complete enough for another analyst to reproduce findings

## References

- [crt.sh Certificate Search](https://crt.sh/)
- [Certstream Real-Time CT Monitor](https://certstream.calidog.io/)
- [River Security: CT Logs for Attack Surface Discovery](https://riversecurity.eu/finding-attack-surface-and-fraudulent-domains-via-certificate-transparency-logs/)
- [Let's Encrypt: Certificate Transparency Logs](https://letsencrypt.org/docs/ct-logs/)
- [SSLMate Cert Spotter](https://sslmate.com/certspotter/)
- [CyberSierra: CT Logs as Early Warning System](https://cybersierra.co/blog/ssl-certificate-transparency-logs/)

## Process

1. Analyze the task requirements
2. Apply domain expertise
3. Verify output quality

## Anti-Rationalization Table

| Rationalization | Reality |
|---|---|
| "We are too small to be targeted" | Automated attacks target everyone. Size does not matter. |
| "Security slows us down" | A breach slows you down 100x more. Build security in from the start. |
| "We will fix it after launch" | Vulnerabilities in production are exploited within hours. Fix before deploy. |