--- name: building-threat-actor-profile-from-osint-4e2f4b299fe4 description: Build comprehensive threat actor profiles using open-source intelligence (OSINT) techniques to document adversary motivations, capabilities, infrastructure, and TTPs for proactive defense. domain: cybersecurity subdomain: threat-intelligence tags: - osint - threat-actor - profiling - maltego - spiderfoot - attribution - threat-intelligence - reconnaissance version: '1.0' author: Krishna Kumar (xalgord) license: Apache-2.0 nist_csf: - ID.RA-01 - ID.RA-05 - DE.CM-01 - DE.AE-02 --- # Building Threat Actor Profile from OSINT ## Overview Threat actor profiling using OSINT systematically gathers and analyzes publicly available information to build comprehensive profiles of adversary groups. This skill covers collecting intelligence from public sources (security vendor reports, paste sites, dark web forums, social media, code repositories), correlating indicators across platforms, mapping adversary infrastructure using tools like Maltego and SpiderFoot, and producing structured threat actor dossiers that inform defensive strategies and attribution assessments. ## When to Use - When deploying or configuring building threat actor profile from osint capabilities in your environment - When establishing security controls aligned to compliance requirements - When building or improving security architecture for this domain - When conducting security assessments that require this implementation ## Common Misconfigurations & Verification - **Conflating Threat Actor and Intrusion Set SDOs:** OSINT describes activity (Intrusion Set) but analysts often create only a `threat-actor` SDO with a guessed human identity. Model observed activity as `intrusion-set`, link it to a `threat-actor` via an `attributed-to` SRO, and keep `confidence` on that relationship low until corroborated. - **Alias sprawl without dedup:** the same group appears as Cozy Bear / Midnight Blizzard / NOBELIUM / UNC2452 across vendors. Normalize aliases into one `aliases[]` field and reconcile against the MITRE ATT&CK Groups galaxy (e.g., G0016) instead of spawning duplicate objects. - **Attribution overconfidence:** shared infrastructure (bulletproof hosts, recycled VPS) or reused tooling does not prove a single actor. Apply ACH and set `confidence` numerically (0–100) per STIX; never hardcode "high" from one source. - **Stale OSINT:** WHOIS privacy and fast-flux rotate; check passive-DNS and cert-transparency timestamps and stamp `valid_from`/`first_seen` rather than the collection date. - **Verify:** round-trip the bundle through `stix2.parse(allow_custom=False)`, confirm every `created_by_ref`/`source_ref`/`target_ref` resolves within the bundle, and confirm each mapped TTP carries a real ATT&CK technique ID before publishing. ## Prerequisites - Python 3.9+ with `shodan`, `requests`, `beautifulsoup4`, `maltego-trx`, `stix2` libraries - SpiderFoot (https://github.com/smicallef/spiderfoot) or SpiderFoot HX - Maltego CE or Maltego XL for link analysis - API keys: Shodan, VirusTotal, AlienVault OTX, PassiveTotal/RiskIQ - MITRE ATT&CK knowledge for TTP mapping - Understanding of STIX 2.1 Intrusion Set, Threat Actor, and Identity SDOs ## Key Concepts ### OSINT Sources for Threat Actor Profiling Primary intelligence sources include vendor threat reports (Mandiant, CrowdStrike, Recorded Future, Talos), government advisories (CISA, NSA, FBI joint advisories), academic research papers, malware repositories (VirusTotal, MalwareBazaar, Malpedia), paste sites (Pastebin, GitHub Gists), code repositories, social media accounts, dark web forums, and certificate transparency logs. ### Structured Analytical Techniques Profiling uses the Diamond Model (adversary, infrastructure, capability, victim), Analysis of Competing Hypotheses (ACH) for attribution confidence, and MITRE ATT&CK mapping for TTP documentation. Link analysis tools like Maltego visualize relationships between indicators, infrastructure, and actors. ### Profile Components A complete threat actor profile includes: aliases and naming conventions across vendors, suspected origin and sponsorship, motivation (espionage, financial, hacktivism, disruption), targeted sectors and geographies, known campaigns and operations, TTPs mapped to ATT&CK, toolset and malware families, infrastructure patterns, and historical timeline. ## Workflow ### Step 1: Collect Intelligence from Multiple Sources ```python import requests import json from datetime import datetime class OSINTCollector: def __init__(self, vt_key=None, otx_key=None, shodan_key=None): self.vt_key = vt_key self.otx_key = otx_key self.shodan_key = shodan_key self.collected_data = {"sources": [], "indicators": [], "reports": []} def search_alienvault_otx(self, actor_name): """Search AlienVault OTX for threat actor pulses.""" headers = {"X-OTX-API-KEY": self.otx_key} url = f"https://otx.alienvault.com/api/v1/search/pulses?q={actor_name}&limit=20" resp = requests.get(url, headers=headers) if resp.status_code == 200: data = resp.json() pulses = data.get("results", []) for pulse in pulses: self.collected_data["reports"].append({ "source": "AlienVault OTX", "title": pulse.get("name", ""), "created": pulse.get("created", ""), "description": pulse.get("description", "")[:500], "tags": pulse.get("tags", []), "indicators_count": len(pulse.get("indicators", [])), "pulse_id": pulse.get("id", ""), }) for ioc in pulse.get("indicators", []): self.collected_data["indicators"].append({ "type": ioc.get("type", ""), "value": ioc.get("indicator", ""), "source": "OTX", "pulse": pulse.get("name", ""), }) print(f"[+] OTX: Found {len(pulses)} pulses for '{actor_name}'") return self.collected_data def search_virustotal_collections(self, actor_name): """Search VirusTotal for threat actor collections.""" headers = {"x-apikey": self.vt_key} url = "https://www.virustotal.com/api/v3/intelligence/search" params = {"query": f"tag:{actor_name.lower().replace(' ', '-')}"} resp = requests.get(url, headers=headers, params=params) if resp.status_code == 200: results = resp.json().get("data", []) print(f"[+] VT: Found {len(results)} samples tagged '{actor_name}'") return results return [] def query_shodan_infrastructure(self, indicators): """Query Shodan for infrastructure details on IPs.""" results = [] for ip in indicators: url = f"https://api.shodan.io/shodan/host/{ip}?key={self.shodan_key}" resp = requests.get(url) if resp.status_code == 200: data = resp.json() results.append({ "ip": ip, "org": data.get("org", ""), "asn": data.get("asn", ""), "country": data.get("country_code", ""), "ports": data.get("ports", []), "hostnames": data.get("hostnames", []), "os": data.get("os", ""), "last_update": data.get("last_update", ""), }) print(f"[+] Shodan: Enriched {len(results)} IPs") return results collector = OSINTCollector( vt_key="YOUR_VT_KEY", otx_key="YOUR_OTX_KEY", shodan_key="YOUR_SHODAN_KEY", ) data = collector.search_alienvault_otx("APT29") ``` ### Step 2: Build Structured Threat Actor Profile ```python from stix2 import ThreatActor, IntrusionSet, Identity, Relationship, Bundle from datetime import datetime # Create STIX 2.1 Threat Actor profile identity = Identity( name="Cybersecurity Analyst", identity_class="individual", ) threat_actor = ThreatActor( name="APT29", description="APT29 (also known as Cozy Bear, Midnight Blizzard, NOBELIUM, The Dukes) " "is a Russian state-sponsored threat group attributed to Russia's Foreign " "Intelligence Service (SVR). Active since at least 2008, the group conducts " "cyber espionage targeting government, diplomatic, think tank, healthcare, " "and energy organizations primarily in NATO countries.", aliases=["Cozy Bear", "Midnight Blizzard", "NOBELIUM", "The Dukes", "Dark Halo", "UNC2452", "YTTRIUM", "Blue Kitsune", "Iron Ritual"], roles=["agent"], sophistication="strategic", resource_level="government", primary_motivation="organizational-gain", secondary_motivations=["ideology"], threat_actor_types=["nation-state"], goals=["Intelligence collection on foreign governments", "Long-term persistent access to high-value targets", "Supply chain compromise for broad access"], created_by_ref=identity.id, ) intrusion_set = IntrusionSet( name="APT29", description="Intrusion set tracked as APT29, attributed to Russian SVR.", aliases=["Cozy Bear", "Midnight Blizzard"], first_seen="2008-01-01T00:00:00Z", goals=["espionage"], resource_level="government", primary_motivation="organizational-gain", ) relationship = Relationship( relationship_type="attributed-to", source_ref=intrusion_set.id, target_ref=threat_actor.id, ) bundle = Bundle(objects=[identity, threat_actor, intrusion_set, relationship]) with open("apt29_profile.json", "w") as f: f.write(bundle.serialize(pretty=True)) print("[+] STIX profile saved: apt29_profile.json") ``` ### Step 3: Map TTPs to MITRE ATT&CK ```python from attackcti import attack_client lift = attack_client() apt29_techs = lift.get_techniques_used_by_group("G0016") profile_ttps = { "initial_access": [], "execution": [], "persistence": [], "defense_evasion": [], "credential_access": [], "lateral_movement": [], "collection": [], "c2": [], "exfiltration": [], } tactic_mapping = { "initial-access": "initial_access", "execution": "execution", "persistence": "persistence", "defense-evasion": "defense_evasion", "credential-access": "credential_access", "lateral-movement": "lateral_movement", "collection": "collection", "command-and-control": "c2", "exfiltration": "exfiltration", } for tech in apt29_techs: tech_id = "" for ref in tech.get("external_references", []): if ref.get("source_name") == "mitre-attack": tech_id = ref.get("external_id", "") break for phase in tech.get("kill_chain_phases", []): tactic = phase.get("phase_name", "") key = tactic_mapping.get(tactic) if key: profile_ttps[key].append({ "id": tech_id, "name": tech.get("name", ""), "description": tech.get("description", "")[:200], }) print("=== APT29 TTP Profile ===") for tactic, techs in profile_ttps.items(): if techs: print(f"\n{tactic.upper()} ({len(techs)} techniques):") for t in techs[:5]: print(f" {t['id']}: {t['name']}") ``` ### Step 4: Correlate Infrastructure with SpiderFoot ```python import subprocess import json def run_spiderfoot_scan(target, scan_name="actor_recon"): """Run SpiderFoot scan against target domain or IP.""" cmd = [ "python3", "-m", "spiderfoot", "-s", target, "-m", "sfp_dns,sfp_whois,sfp_shodan,sfp_virustotal,sfp_certspotter", "-o", "json", "-q", ] result = subprocess.run(cmd, capture_output=True, text=True, timeout=300) if result.returncode == 0: findings = json.loads(result.stdout) if result.stdout else [] print(f"[+] SpiderFoot: {len(findings)} findings for {target}") return findings return [] def correlate_infrastructure(indicators): """Find relationships between infrastructure indicators.""" ip_to_domains = {} domain_to_ips = {} registrar_patterns = {} for indicator in indicators: ioc_type = indicator.get("type", "") value = indicator.get("value", "") if ioc_type == "IP_ADDRESS": if value not in ip_to_domains: ip_to_domains[value] = set() elif ioc_type == "INTERNET_NAME": if value not in domain_to_ips: domain_to_ips[value] = set() # Identify shared hosting, registration patterns shared_ips = {ip: domains for ip, domains in ip_to_domains.items() if len(domains) > 1} print(f"[+] Shared infrastructure IPs: {len(shared_ips)}") return {"shared_ips": shared_ips, "registrar_patterns": registrar_patterns} ``` ### Step 5: Generate Threat Actor Dossier ```python def generate_dossier(actor_name, profile_data, ttp_data, infrastructure_data): dossier = f"""# Threat Actor Dossier: {actor_name} ## Generated: {datetime.now().isoformat()} ## Executive Summary {profile_data.get('description', '')} ## Attribution - **Suspected Origin**: {profile_data.get('origin', 'Unknown')} - **Sponsorship**: {profile_data.get('sponsorship', 'Unknown')} - **Confidence Level**: {profile_data.get('confidence', 'Medium')} - **First Observed**: {profile_data.get('first_seen', 'Unknown')} ## Aliases {', '.join(profile_data.get('aliases', []))} ## Targeting - **Sectors**: {', '.join(profile_data.get('sectors', []))} - **Regions**: {', '.join(profile_data.get('regions', []))} - **Motivation**: {profile_data.get('motivation', 'Unknown')} ## TTP Summary (MITRE ATT&CK) """ for tactic, techs in ttp_data.items(): if techs: dossier += f"\n### {tactic.replace('_', ' ').title()}\n" for t in techs: dossier += f"- **{t['id']}**: {t['name']}\n" dossier += f""" ## Infrastructure Patterns - Known C2 servers: {len(infrastructure_data.get('c2_servers', []))} - Domain patterns: {', '.join(infrastructure_data.get('domain_patterns', []))} - Hosting preferences: {', '.join(infrastructure_data.get('hosting', []))} ## Recommendations 1. Monitor for known TTPs in EDR/SIEM 2. Block known infrastructure indicators 3. Hunt for behavioral patterns in network traffic 4. Implement detections for top technique gaps """ with open(f"{actor_name.lower().replace(' ', '_')}_dossier.md", "w") as f: f.write(dossier) print(f"[+] Dossier saved for {actor_name}") generate_dossier("APT29", { "description": "Russian state-sponsored espionage group attributed to SVR", "origin": "Russia", "sponsorship": "SVR (Foreign Intelligence Service)", "confidence": "High", "first_seen": "2008", "aliases": ["Cozy Bear", "Midnight Blizzard", "NOBELIUM", "The Dukes"], "sectors": ["Government", "Diplomatic", "Think Tank", "Healthcare", "Energy"], "regions": ["North America", "Europe", "NATO countries"], "motivation": "Espionage", }, profile_ttps, {"c2_servers": [], "domain_patterns": [], "hosting": []}) ``` ## Validation Criteria - Intelligence collected from at least 3 OSINT sources - STIX 2.1 Threat Actor and Intrusion Set objects created correctly - TTPs mapped to ATT&CK with technique IDs and procedure examples - Infrastructure indicators correlated across sources - Dossier includes attribution assessment with confidence levels - Profile is actionable for detection engineering and threat hunting ## References - [Huntress: Threat Actor Profiling](https://www.huntress.com/cybersecurity-101/topic/threat-actor-profiling) - [CrowdStrike: OSINT in Cybersecurity](https://www.crowdstrike.com/en-us/cybersecurity-101/threat-intelligence/open-source-intelligence-osint/) - [SpiderFoot OSINT Tool](https://github.com/smicallef/spiderfoot) - [MITRE ATT&CK Groups](https://attack.mitre.org/groups/) - [ShadowDragon: OSINT Techniques](https://shadowdragon.io/blog/osint-techniques/) - [ISACA: Building a Threat-Led Cybersecurity Program](https://www.isaca.org/resources/white-papers/2025/building-a-threat-led-cybersecurity-program) --- Source attribution: xalgord Original project: https://github.com/xalgord/xalgorix/blob/cec44d71b1d998208e1e2091982d0ee2f928041f/internal/tools/skills/data/threat-intelligence/building-threat-actor-profile-from-osint/SKILL.md License: Apache-2.0 License information: https://www.apache.org/licenses/LICENSE-2.0 Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. "Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, "control" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. "You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License. "Source" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files. "Object" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types. "Work" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below). "Derivative Works" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof. "Contribution" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, "submitted" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as "Not a Contribution." "Contributor" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Work. 2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form. 3. Grant of Patent License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed. 4. Redistribution. You may reproduce and distribute copies of the Work or Derivative Works thereof in any medium, with or without modifications, and in Source or Object form, provided that You meet the following conditions: (a) You must give any other recipients of the Work or Derivative Works a copy of this License; and (b) You must cause any modified files to carry prominent notices stating that You changed the files; and (c) You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works; and (d) If the Work includes a "NOTICE" text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file, excluding those notices that do not pertain to any part of the Derivative Works, in at least one of the following places: within a NOTICE text file distributed as part of the Derivative Works; within the Source form or documentation, if provided along with the Derivative Works; or, within a display generated by the Derivative Works, if and wherever such third-party notices normally appear. The contents of the NOTICE file are for informational purposes only and do not modify the License. You may add Your own attribution notices within Derivative Works that You distribute, alongside or as an addendum to the NOTICE text from the Work, provided that such additional attribution notices cannot be construed as modifying the License. You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions for use, reproduction, or distribution of Your modifications, or for any such Derivative Works as a whole, provided Your use, reproduction, and distribution of the Work otherwise complies with the conditions stated in this License. 5. Submission of Contributions. Unless You explicitly state otherwise, any Contribution intentionally submitted for inclusion in the Work by You to the Licensor shall be under the terms and conditions of this License, without any additional terms or conditions. Notwithstanding the above, nothing herein shall supersede or modify the terms of any separate license agreement you may have executed with Licensor regarding such Contributions. 6. Trademarks. This License does not grant permission to use the trade names, trademarks, service marks, or product names of the Licensor, except as required for reasonable and customary use in describing the origin of the Work and reproducing the content of the NOTICE file. 7. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Work (and each Contributor provides its Contributions) on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Work and assume any risks associated with Your exercise of permissions under this License. 8. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Work (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages. 9. Accepting Warranty or Additional Liability. While redistributing the Work or Derivative Works thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability. END OF TERMS AND CONDITIONS APPENDIX: How to apply the Apache License to your work. To apply the Apache License to your work, attach the following boilerplate notice, with the fields enclosed by brackets "[]" replaced with your own identifying information. (Don't include the brackets!) The text should be enclosed in the appropriate comment syntax for the file format. We also recommend that a file or class name and description of purpose be included on the same "printed page" as the copyright notice for easier identification within third-party archives. Copyright 2026 xalgord Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. # Mega AI Hub resource metadata Creator / publisher: xalgord Original project: https://github.com/xalgord/xalgorix/blob/cec44d71b1d998208e1e2091982d0ee2f928041f/internal/tools/skills/data/threat-intelligence/building-threat-actor-profile-from-osint/SKILL.md Attribution basis: Source repository record Fields of work: Data & Analytics, Development & Engineering, Education & Research, Sales Roles: Business Development, Data Analyst, ML / AI Engineer, Researcher, Security Engineer Specialties: Specialty not specified Tools: Alienvault, Passivetotal, Shodan, Virustotal Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 3861fcfe61c887f276fa1d28d609b418e5276da7a81aed753d12d6743976cfed