external/anthropic-cybersecurity-skills/skills/profiling-threat-actor-groups/SKILL.md
Develops comprehensive threat actor profiles for APT groups, criminal organizations, and hacktivist collectives by aggregating TTP documentation, historical campaign data, tooling fingerprints, and attribution indicators from multiple intelligence sources. Use when briefing executives on sector-specific threats, updating threat model assumptions, or prioritizing defensive controls against specific adversaries. Activates for requests involving MITRE ATT&CK Groups, Mandiant APT profiles, CrowdStrike adversary naming, or sector-specific threat briefings.
npx skillsauth add seikaikyo/dash-skills profiling-threat-actor-groupsInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Use this skill when:
Do not use this skill for real-time incident attribution — attribution during active incidents should be deprioritized in favor of containment. Profile refinement occurs post-incident.
Cross-reference your organization's sector, geography, and technology stack against known adversary targeting patterns. Sources:
Shortlist 5–10 groups most likely to target your organization based on sector alignment and recent activity.
For each adversary, document across standard dimensions:
Identity: ATT&CK Group ID (e.g., G0016 for APT29), aliases (Cozy Bear, The Dukes, Midnight Blizzard), suspected nation-state sponsor
Motivations: Espionage, financial gain, disruption, intellectual property theft
Targeting: Sectors, geographies, organization sizes, technology targets (OT/IT, cloud, supply chain)
Capabilities: Custom malware (e.g., APT29's SUNBURST, MiniDuke), exploitation of 0-days vs. known CVEs, supply chain attack capability
Campaign History: Notable operations with dates (SolarWinds 2020, Exchange Server 2021, etc.)
TTPs by ATT&CK Phase: Document top 5 techniques per tactic phase
Using mitreattack-python:
from mitreattack.stix20 import MitreAttackData
mitre = MitreAttackData("enterprise-attack.json")
apt29 = mitre.get_object_by_attack_id("G0016", "groups")
techniques = mitre.get_techniques_used_by_group(apt29)
profile = {}
for item in techniques:
tech = item["object"]
tid = tech["external_references"][0]["external_id"]
tactic = [p["phase_name"] for p in tech.get("kill_chain_phases", [])]
profile[tid] = {"name": tech["name"], "tactics": tactic}
Compare the adversary's technique list against your detection coverage matrix (from ATT&CK Navigator layer). Identify:
Structure the final profile for different audiences:
Classify TLP:AMBER for internal distribution; seek ISAC approval before external sharing.
| Term | Definition | |------|-----------| | APT | Advanced Persistent Threat — well-resourced, sophisticated adversary (typically nation-state or sophisticated criminal) conducting long-term targeted operations | | TTPs | Tactics, Techniques, Procedures — behavioral fingerprint of an adversary group, more durable than IOCs which change frequently | | Aliases | Threat actors receive different names from different vendors (APT29 = Cozy Bear = The Dukes = Midnight Blizzard = YTTRIUM) | | Attribution | Process of associating an attack with a specific threat actor; requires multiple independent corroborating data points and carries inherent uncertainty | | Cluster | A group of related intrusion activity that may or may not be attributable to a single actor; used when attribution is uncertain | | Intrusion Set | STIX SDO type representing a grouped set of adversarial behaviors with common objectives, even if actor identity is unknown |
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.