external/anthropic-cybersecurity-skills/skills/hunting-for-dns-based-persistence/SKILL.md
Hunts for DNS-based persistence mechanisms such as DNS hijacking, dangling CNAME records enabling subdomain takeover, wildcard DNS abuse, and unauthorized zone or NS delegation changes, using passive DNS history (SecurityTrails API), Route53/Azure DNS/Cloudflare audit logs, and zone transfer analysis. Use when investigating suspected DNS hijacking or subdomain takeover, or when threat hunting for DNS record tampering that persists across credential rotations and endpoint reimaging.
npx skillsauth add seikaikyo/dash-skills hunting-for-dns-based-persistenceInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
Attackers establish DNS-based persistence by hijacking DNS records, creating unauthorized subdomains, abusing wildcard DNS entries, or modifying NS delegations to redirect traffic through attacker-controlled infrastructure. These techniques survive credential rotations, endpoint reimaging, and traditional remediation because DNS changes persist independently of compromised hosts. Detection requires passive DNS historical analysis, zone file auditing, and monitoring for unauthorized record modifications. This skill covers hunting methodologies using SecurityTrails passive DNS API, DNS audit logs from Route53/Azure DNS/Cloudflare, and zone transfer analysis.
Export current DNS zone records and establish baseline for all authorized A, AAAA, CNAME, MX, NS, and TXT records.
Use SecurityTrails API to retrieve historical DNS records and identify unauthorized changes, new subdomains, and CNAME records pointing to decommissioned services (dangling CNAMEs).
Compare current records against baseline to identify unauthorized modifications, wildcard records that resolve all subdomains, NS delegation changes, and MX record hijacking.
Correlate DNS anomalies with threat intelligence feeds, check resolution targets against known malicious infrastructure, and validate record ownership.
JSON report listing DNS anomalies with record type, historical changes, risk severity, and remediation recommendations for each finding.
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.