external/anthropic-cybersecurity-skills/skills/hunting-for-domain-fronting-c2-traffic/SKILL.md
Detects domain fronting C2 traffic by analyzing SNI-vs-HTTP-Host-header mismatches in proxy logs and inspecting TLS certificate discrepancies with pyOpenSSL. Use when hunting for command-and-control traffic hidden behind legitimate CDN domains, or when investigating proxy/TLS logs for signs of domain fronting evasion.
npx skillsauth add seikaikyo/dash-skills hunting-for-domain-fronting-c2-trafficInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Domain fronting (MITRE ATT&CK T1090.004) is a technique where attackers use different domain names in the TLS SNI field and the HTTP Host header to disguise C2 traffic behind legitimate CDN-hosted domains. This skill detects domain fronting by parsing proxy/web gateway logs for SNI-Host header mismatches, analyzing TLS certificates for CDN provider identification, flagging connections where the SNI points to a high-reputation domain but the Host header targets an attacker-controlled domain, and correlating with known CDN provider IP ranges.
JSON report containing detected domain fronting indicators with SNI-Host pairs, certificate details, CDN provider identification, confidence scores, and MITRE ATT&CK technique mapping.
tools
Conduct comprehensive GDPR compliance assessments by evaluating data processing activities against EU Regulation 2016/679, including Article 30 records of processing, lawful basis validation, data subject rights implementation, Data Protection Impact Assessments (DPIAs) under Article 35, breach notification procedures, international transfer safeguards (SCCs, adequacy decisions), and technical/organizational measures under Article 32. Use when processing personal data of EU residents, preparing for supervisory authority audits, implementing privacy-by-design for new systems, scoping compliance gaps for M&A due diligence, assessing third-party processors, or responding to data subject access requests at scale. Incorporates 2026 guidance from ICO, EDPB, and post-Data (Use and Access) Act 2025 UK-GDPR considerations. Do not use for implementing specific Article 32 controls — use implementing-gdpr-data-protection-controls; or for DSAR automation — use implementing-gdpr-data-subject-access-request.
tools
Parse Windows forensic artifacts—$MFT/$J (MFTECmd), Prefetch (PECmd), registry hives (RECmd), shellbags, and Amcache—into normalized CSV/JSON with Eric Zimmerman's EZ Tools, then load results into Timeline Explorer for analysis. Use during DFIR/incident-response investigations, after triage collection (e.g. with KAPE), to establish program execution, file/folder access, and persistence evidence from acquired forensic images.
development
Build automated multi-turn adversarial attacks against conversational LLM targets using Microsoft PyRIT's RedTeamingOrchestrator, CrescendoOrchestrator (gradual escalation), and TreeOfAttacksWithPruningOrchestrator (adaptive branching), with scorer feedback loops and persisted conversation memory. Use when single-shot LLM scanning is insufficient and you need multi-turn, scorer-driven AI red-team campaigns against a chatbot or agent.
testing
Stand up MISP, enable and cache curated threat feeds (CIRCL, abuse.ch, Feodo Tracker), apply warninglists to suppress false positives, query indicators with PyMISP, and export attributes as auto-generated Suricata/Sigma/Wazuh detection rules. Use when maturing a MISP instance to actively drive detection, curating threat feeds with quality controls, or automating IOC-to-detection pipelines for the SIEM/IDS.