Threat Actor Attribution logo

Threat Actor Attribution

Organization
gensecaihq
threat-actor-attribution

Assess possible threat-actor or campaign links using the Diamond Model and ATT&CK group data, expressed with calibrated estimative language; use only when a case shows enough TTP or infrastructure overlap to justify attribution discussion.

Overview

Publishergensecaihq
RepositoryWazuh-Autopilot
Skill namethreat-actor-attribution
Stars
61
Forks
15
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by gensecaihq on GitHub. Read the source before you install it.

Installation

Install the Threat Actor Attribution AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/gensecaihq/Wazuh-Autopilot/tree/main/backend/app/skills/threat-actor-attribution
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/gensecaihq/Wazuh-Autopilot.git /tmp/Wazuh-Autopilot
mkdir -p .claude/skills
cp -r /tmp/Wazuh-Autopilot/backend/app/skills/threat-actor-attribution .claude/skills/threat-actor-attribution
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Threat Actor Attribution in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Threat Actor Attribution on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Threat Actor Attribution is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Threat Actor Attribution

Attribution is rarely needed to respond, and wrong attribution causes real harm (misdirected response, bad executive decisions). Do it carefully or not at all.

When to attempt

Attempt only if at least two of these hold:

  • Infrastructure (IP/domain) with a reliable (A/B) public link to a named cluster.
  • Tooling or malware family with known actor associations.
  • A distinctive TTP sequence matching an ATT&CK group profile.
  • Targeting consistent with the actor's known sector/geography interest.

Otherwise record "attribution not assessed — insufficient evidence" and stop.

Diamond Model

Describe the intrusion on four vertices and the links between them:

VertexQuestionsEvidence sources
AdversaryWho operates it? (often unknown — that's fine)intel reporting
CapabilityTools, malware, exploits, techniqueshost forensics, ATT&CK mapping
InfrastructureIPs, domains, C2, hostingIOC enrichment
VictimWhich assets, users, sectorcase entities, org context

Meta-features: timestamp, phase (ATT&CK tactic), result, direction, methodology. Pivot vertex-to-vertex (e.g. infrastructure → other victims seen in intel) to test links.

Comparing to ATT&CK groups

  1. List the case's confirmed techniques (from mitre-attack-mapping).
  2. Compare with candidate groups' technique sets from public ATT&CK group pages (via search_external_context for the group name — public data only).
  3. Overlap on common techniques (T1059, T1078, T1110) means little; weight distinctive techniques and tool names higher.
  4. Record competing hypotheses, including "unknown / commodity actor".

Estimative language (ICD 203 style)

Use consistent probability terms and state confidence separately:

TermApprox. probability
almost no chance / remote1–5%
very unlikely5–20%
unlikely20–45%
roughly even chance45–55%
likely55–80%
very likely80–95%
almost certainly95–99%

Confidence (low / moderate / high) reflects evidence quality and source reliability, not probability. Example: "We assess it is unlikely (moderate confidence) that this activity is linked to group X; infrastructure overlap is limited to a shared hosting provider."

Rules

  • Never name a nation-state sponsor on your own; at most reference public reporting and grade it.
  • Distinguish commodity crimeware, opportunistic scanning, and targeted operations.
  • Attribution never changes containment urgency by itself.

Output

  • add_finding titled Attribution assessment with the Diamond Model table, competing hypotheses with estimative language and confidence, sources with Admiralty grades, and key gaps. standard_refs: MITRE-ATTACK.

Frequently asked questions

What does the Threat Actor Attribution AI skill do?

Assess possible threat-actor or campaign links using the Diamond Model and ATT&CK group data, expressed with calibrated estimative language; use only when a case shows enough TTP or infrastructure overlap to justify attribution discussion.

Why use Threat Actor Attribution on TypingMind?

Because you install it once and use it with any model. Threat Actor Attribution is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Threat Actor Attribution in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/gensecaihq/Wazuh-Autopilot/tree/main/backend/app/skills/threat-actor-attribution. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Threat Actor Attribution?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Threat Actor Attribution?

As many as you like. As long as a model supports skills, you can use Threat Actor Attribution with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Threat Actor Attribution AI skill free?

Yes. It is published on GitHub by gensecaihq under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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