analyzing-tls-certificate-transparency-logs
This skill queries Certificate Transparency logs through crt.sh and pycrtsh to identify suspicious TLS certificates issued for domains resembling an organization's brand. Use it during security incident investigations, threat hunting operations, and SOC monitoring to detect typosquatting variations, unauthorized wildcard certificates, and phishing infrastructure by analyzing certificate patterns and issuers.
git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills /tmp/analyzing-tls-certificate-transparency-logs && cp -r /tmp/analyzing-tls-certificate-transparency-logs/skills/analyzing-tls-certificate-transparency-logs ~/.claude/skills/analyzing-tls-certificate-transparency-logsSKILL.md
# Analyzing TLS Certificate Transparency Logs
## When to Use
- When investigating security incidents that require analyzing tls certificate transparency logs
- 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
- Familiarity with security operations concepts and tools
- Access to a test or lab environment for safe execution
- Python 3.8+ with required dependencies installed
- Appropriate authorization for any testing activities
## Instructions
Query crt.sh Certificate Transparency database to find certificates issued for
domains similar to your organization's brand, detecting phishing infrastructure.
```python
from pycrtsh import Crtsh
c = Crtsh()
# Search for certificates matching a domain
certs = c.search("example.com")
for cert in certs:
print(cert["id"], cert["name_value"])
# Get full certificate details
details = c.get(certs[0]["id"], type="id")
```
Key analysis steps:
1. Query crt.sh for all certificates matching your domain pattern
2. Identify certificates with typosquatting variations (Levenshtein distance)
3. Flag certificates from unexpected CAs
4. Monitor for wildcard certificates on suspicious subdomains
5. Cross-reference with known phishing infrastructure
## Examples
```python
from pycrtsh import Crtsh
c = Crtsh()
certs = c.search("%.example.com")
for cert in certs:
print(f"Issuer: {cert.get('issuer_name')}, Domain: {cert.get('name_value')}")
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