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.
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name
analyzing-certificate-transparency-for-phishing
description
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.
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
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
Scope the task — define objectives, boundaries, and success criteria
Gather information — collect all necessary data and context before proceeding
Execute the core workflow — follow the domain-specific steps methodically
Validate results — verify outputs against expected outcomes or baselines
Document findings — record results, anomalies, and recommendations
Step 1: Query crt.sh for Certificate History
import requests
import json
from datetime import datetime
import tldextract
classCTLogMonitor:
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]
defquery_crt_sh(self, domain, include_expired=False):
"""Query crt.sh for certificates matching a domain."""
params = {
"q": f"%.{domain}",
"output": "json",
}
ifnot 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 []
deffind_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")
ifany(kw in common_name for kw inself.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
import certstream
import Levenshtein
import re
from datetime import datetime
classCertstreamMonitor:
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 = []
defstart_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}")
defcallback(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("*.")
ifself._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']}")
iflen(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.")
returnself.alerts
def_is_suspicious(self, domain):
"""Check if domain is suspicious relative to watched domains."""for watched inself.watched_domains:
# Exact keyword match
watched_base = watched.split(".")[0]
if watched_base in domain and domain != watched:
returnTrue# 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:
returnTrue# Brand keyword matchfor keyword inself.brand_keywords:
if keyword in domain:
returnTruereturnFalsedef_get_reason(self, domain):
"""Determine why domain was flagged."""
reasons = []
for watched inself.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 inself.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
defenumerate_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
