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分析TLS证书透明度日志

通过crt.sh和pycrtsh查询证书透明度日志,检测钓鱼基础设施和品牌仿冒。

分析TLS证书透明度日志 是什么?

该技能用于自动化查询crt.sh证书透明度数据库,发现与目标组织品牌域名相似的已签发证书,从而有效检测钓鱼攻击和品牌仿冒基础设施。它基于Python 3.8+和pycrtsh库实现,支持通过通配符、编辑距离等方式进行域名变体识别,并标记来自异常证书颁发机构的证书。适用于事件响应、威胁狩猎、检测规则构建和安全监控覆盖验证等场景,尤其适合在测试或实验室环境中安全执行。

兼容平台~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/analyzing-tls-certificate-transparency-logs

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文档

分析TLS证书透明度日志 是做什么的?

何时使用

  • 调查需要分析TLS证书透明度日志的安全事件时
  • 为此领域构建检测规则或威胁狩猎查询时
  • SOC分析师需要此类分析的结构化流程时
  • 验证相关攻击技术安全监控覆盖范围时

前提条件

  • 熟悉安全运维概念和工具
  • 拥有用于安全执行的测试或实验环境
  • 已安装Python 3.8+及所需依赖项
  • 具备任何测试活动的适当授权

操作说明

查询crt.sh证书透明度数据库,查找为与组织品牌相似的域名签发的证书,检测钓鱼基础设施。

from pycrtsh import Crtsh

c = Crtsh()
# 搜索匹配某个域名的证书
certs = c.search("example.com")
for cert in certs:
    print(cert["id"], cert["name_value"])

# 获取完整证书详情
details = c.get(certs[0]["id"], type="id")

关键分析步骤:

  1. 查询crt.sh获取匹配你域名模式的所有证书
  2. 识别存在拼写变异(编辑距离)的证书
  3. 标记来自异常证书颁发机构的证书
  4. 监控可疑子域名上的通配符证书
  5. 与已知钓鱼基础设施交叉引用

示例

from pycrtsh import Crtsh
c = Crtsh()
certs = c.search("%.example.com")
for cert in certs:
    print(f"签发者: {cert.get('issuer_name')}, 域名: {cert.get('name_value')}")

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

abusing-dpapi-for-credential-access

Extract and decrypt Windows DPAPI-protected secrets (Credential Manager, browser logins/cookies, Wi-Fi credentials, KeePass keys) online or offline using SharpDPAPI, SharpChrome, Mimikatz, or Impacket

abusing-shadow-credentials-for-privesc

Take over Active Directory accounts by writing attacker-controlled public keys to msDS-KeyCredentialLink (Shadow Credentials) with pyWhisker, Whisker, or Certipy, then authenticate via PKINIT to recover the target

achieving-cmmc-level-2-compliance

>-

acquiring-disk-image-with-dd-and-dcfldd

Create forensically sound bit-for-bit disk images with dd or dcfldd on a Linux forensic workstation, preserving evidence integrity through hash verification (MD5/SHA) during acquisition. Use when imaging a suspect drive, USB device, or memory card for investigation, preserving volatile disk evidence during incident response, or producing a verified copy for legal or law-enforcement proceedings before any destructive analysis.

analyzing-active-directory-acl-abuse

Detect dangerous ACL misconfigurations in Active Directory using ldap3

analyzing-android-malware-with-apktool

Perform static analysis of Android APK malware using apktool for resource decompilation, jadx for Java source recovery, and androguard for manifest inspection, dangerous permission-combination detection, and identification of obfuscated code, dynamic code loading, and reflection-based API calls. Use to statically triage a suspicious APK without executing it or to build mobile malware detection rules.

analyzing-api-gateway-access-logs

Parses API Gateway access logs (AWS API Gateway, Kong, Nginx) to detect

analyzing-apt-group-with-mitre-navigator

Query ATT&CK data with attackcti, mitreattack-python, and stix2, then build MITRE ATT&CK Navigator layers and multi-layer heatmap overlays mapping one or more APT groups

analyzing-azure-activity-logs-for-threats

Queries Azure Monitor activity logs and sign-in logs via azure-monitor-query

analyzing-bootkit-and-rootkit-samples

Analyzes bootkit and advanced rootkit malware infecting the Master

analyzing-browser-forensics-with-hindsight

Parse Chromium-based browser databases with Hindsight to extract and correlate browsing history, downloads, cookies, cached content, autofill data, saved passwords, and extensions from Chrome, Edge, Brave, Opera, and Vivaldi into a unified timeline (XLSX, JSON, or SQLite output). Use during incident response, insider-threat investigations, or criminal cases when you need to reconstruct a user

analyzing-campaign-attribution-evidence

Systematically evaluate cyber-campaign evidence to attribute an operation to a threat actor, using the Diamond Model and Analysis of Competing Hypotheses (ACH) to weigh infrastructure overlaps, TTP consistency, malware code similarity, and timing/language artifacts into confidence-weighted attribution assessments. Use when an incident investigation needs a defensible attribution confidence level.

analyzing-certificate-transparency-for-phishing

Monitor Certificate Transparency logs using crt.sh and Certstream to

analyzing-cloud-storage-access-patterns

Detect abnormal access in AWS S3, GCS, and Azure Blob Storage by analyzing CloudTrail Data Events, GCS audit logs, and Azure Storage Analytics for after-hours bulk downloads, new-IP access, and API-call spikes (e.g. GetObject) via statistical baselines and time-series anomaly detection. Use when investigating suspected cloud data exfiltration or building related detection rules.

analyzing-cobalt-strike-beacon-configuration

Extract and analyze Cobalt Strike beacon configuration from PE files

analyzing-cobaltstrike-malleable-c2-profiles

Parse and analyze Cobalt Strike Malleable C2 profiles with dissect.cobaltstrike (profiles and beacon-payload configs) and pyMalleableC2 (AST parsing) to extract HTTP/DNS transforms, URIs, headers, sleep/jitter, and injection behavior, then generate network detection signatures. Use when reverse-engineering a captured malleable profile or building detections against Cobalt Strike Beacon traffic.

analyzing-command-and-control-communication

Analyzes malware C2 communication over HTTP, HTTPS, DNS, and custom

analyzing-cyber-kill-chain

Analyzes intrusion activity against the Lockheed Martin Cyber Kill Chain

analyzing-disk-image-with-autopsy

Perform comprehensive forensic analysis of raw (dd), E01, or AFF disk images with Autopsy and The Sleuth Kit, recovering deleted files, examining metadata and embedded artifacts, keyword searching, and building investigation timelines with visual reports. Use for structured analysis of a forensic disk image or when stakeholders need visual reports from evidence.

analyzing-dns-logs-for-exfiltration

Analyzes DNS query logs to detect data exfiltration via DNS tunneling,

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