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Develops threat actor profiles for APT groups, criminals, and hacktivists using MITRE ATT&CK, Mandiant, CrowdStrike data on TTPs, campaigns, and tooling. For threat modeling, briefings, and defense prioritization.
Develops threat actor profiles for APT, crime, and hacker groups by aggregating MITRE ATT&CK TTPs, historical activities, tool fingerprints, and intel sources. Useful for threat modeling, management reports, and defense prioritization.
Builds threat actor profiles from OSINT sources like vendor reports, paste sites, and dark web using Maltego, SpiderFoot for motivations, TTPs, infrastructure in cybersecurity.
Share bugs, ideas, or general feedback.
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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 |