Threat modeling

SkillSecurity

Lets your agent find security weaknesses in a system design before it gets built.

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Then ask your AI: use the Threat modeling skill

About this skill

Identifies what could go wrong in a system before it is built or changed, the assets worth attacking, the entry points, the trust boundaries, and the controls that actually address the realistic threats. Use this when designing a feature or system, when a change touches authentication, data handlin

What this skill tells your AI

The instructions your AI receives, as published by cbrock84/headcount in plugins/security/skills/threat-modeling/SKILL.md and read by ahel’s review.

Done at design time this is cheap and changes the design. Done after launch it produces a list of things that are expensive to fix, so the timing is most of the value.

Four questions, in order

1. What are we building? A diagram of the actual data flow — not the org chart, not the marketing architecture. Components, the data moving between them, and where each store lives. If nobody can draw it, that is the first finding.

Mark the trust boundaries: every point where data crosses from something you control to something you do not, or from one privilege level to another. Almost every real vulnerability lives on a boundary.

2. What can go wrong? Walk each boundary and each asset. A usable prompt set:

  • Spoofing — can someone claim to be another user, service, or system?
  • Tampering — can data be modified in transit, at rest, or in the client?
  • Repudiation — can someone deny an action, and would we be able to show otherwise?
  • Information disclosure — what leaks: to other users, to logs, to error messages, to the client bundle?
  • Denial of service — what is unbounded? Uploads, queries, retries, fan-out.
  • Elevation of privilege — can a user reach data or actions belonging to another tenant, role, or account?

Two that catch more real bugs than the classic list: what does the client enforce that the server does not, and what happens on the second attempt — replay, race, and double-submit.

3. What are we going to do about it? For each realistic threat: mitigate, transfer, avoid, or accept. Accepting is legitimate; accepting silently is not.

Prioritize by attacker effort against impact, not by how alarming it sounds. A trivially exploitable tenant-isolation bug outranks a theoretical timing attack every time.

4. Did we do a good job? Re-check the model when the design changes. A threat model that describes last quarter's architecture is worse than none, because it produces false confidence.

Scoping

Model per feature or per boundary, not per system. A whole-system model is too big to finish and too vague to act on.

Timebox it. An hour on a specific feature with the engineers who will build it beats a week-long exercise producing a document nobody reads.

Sources

references/sources.md in this skill lists the outside authorities that settle the questions here — what each one is authoritative for, and what you may do with it. Check them before answering on anything they cover, and cite what you used. Most are free to read and not free to reproduce; the use note on each is binding.

Never

  • Model the system as designed rather than as built. Ask what actually got shipped.
  • Assume internal traffic is trusted. That assumption is what turns one compromised service into an incident.
  • Accept "the framework handles that" without checking that it is configured to.

Signals

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Last commit
Sep 2026
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Item type
skill
Key
threat-modeling-cbrock84
Source
github.com/cbrock84/headcount