Threat Modeler

threat-modeler — figure out what to defend before you defend it

When to use this skill

Trigger when the user wants a planned security view, not a generic scan. Strong signals:

• "threat model this", "do a STRIDE analysis", "what's the attack surface"
• "what could an attacker do here"
• Before any new feature touching auth, payments, file uploads, PII, multi-tenancy, or webhooks
• Before a SOC 2 / ISO 27001 / pen-test engagement
• A new service is being designed and there's no security context yet

Do not trigger for: a static code review (use security-sentinel), incident response (you need a responder), or generic "make this secure" requests where no specific surface has been picked. Pin a scope first.

The output contract

A THREAT_MODEL.md file with two layers:

1. A machine-readable section (front matter or JSON block) — downstream skills (security-sentinel, code-auditor) can parse it to scope what they look for. Lists assets, actors, entry points, trust boundaries, and threats with severity scores.
2. A human-readable section — the actual narrative. A new engineer should be able to read it in 15 minutes and understand what's worth attacking, what's worth defending, and where the team has deliberately accepted a risk.

Plus three concrete deliverables alongside the doc:

• An assumption log — every claim the model rests on, so the next reviewer can verify or invalidate
• An open-questions list — the things the interview couldn't resolve; goes back to the user or to engineering
• A top-5 risk list — the five things to fix this sprint, ranked by likelihood × impact

If the model has 80 threats and no ranking, it's a list, not a model. Rank or don't ship.

Workflow

1 — Pick the frame

Different surfaces fit different frames. Pick one before starting:

• STRIDE (Spoofing, Tampering, Repudiation, Information disclosure, DoS, Elevation of privilege) — for systems and services. The default for "model this API".
• Attack-tree — for one specific high-value flow (payment, signing, identity proofing). The root is the attacker goal; the branches are how they'd get there.
• Abuser stories — for products with end users (multi-tenant SaaS, marketplaces, social). Mirror image of user stories: "as a malicious tenant I want to see other tenants' data so that…"
• LINDDUN — for privacy-heavy systems (Linkability, Identifiability, Non-repudiation, Detectability, Disclosure, Unawareness, Non-compliance). Pull this in in addition to STRIDE when GDPR/HIPAA/biometric data is on the table.

State the choice in one line in the doc. If a system has two distinct surfaces (e.g., a public API + an admin app), do two passes with two frames.

2 — Bootstrap from code (if there is code)

Before asking the user anything, read the code and infer what you can. Goal: walk into the interview with informed questions, not a blank slate.

This pack is web-app focused — the grep recipes below assume Node.js + React (Express / Fastify / Next.js / NestJS on the server; React / Vue / Svelte on the client). For other stacks adapt the patterns; the categories are universal.

For each entry point, identify:

HTTP routes — what's public, what's authenticated, what's admin-only

# Express / Fastify route surface
rg -t ts -t js '\.(get|post|put|patch|delete)\s*\(' src/ | head -40
# Next.js app router
fd 'route\.(ts|js)' app/ | head -20
# Per-route auth middleware (or lack thereof)
rg -B2 'requireAuth|withAuth|isAuthenticated|@UseGuards' src/

Background jobs / cron — what runs on its own authority

rg -t ts -t js 'cron|bullmq|agenda|node-cron|setInterval|setTimeout.*hours?\)'

Webhook handlers — what external callers can trigger

rg -t ts -t js 'webhook|/hooks/|stripe.webhook|sendgrid|svix' src/

Client-side trust — what the React layer assumes about server response

# dangerouslySetInnerHTML is the canonical XSS surface in React
rg 'dangerouslySetInnerHTML' src/
# direct DOM writes that bypass React
rg 'innerHTML\s*=|document\.write' src/
# routes that take user input directly into the URL
rg 'window\.location|router\.push\([^,)]*(query|params)'

File / blob storage — what's user-controlled, what's served back to other users

rg -t ts -t js 'multer|formidable|busboy|S3.*Upload|presigned'

Outbound calls — where user input flows out to other services (SSRF surface)

rg -t ts -t js 'fetch\(|axios\.|undici|got\(' src/ | rg 'req\.|body\.|query\.'

Database access — what queries take user input

# raw SQL with template strings = parameterization audit
rg -t ts -t js 'raw\(|query\(`|sql`' src/
# Mongo: user-controlled keys can become $-operators
rg -t ts -t js '\.find\(.*req\.body|\.findOne\(.*req\.body' src/

Secrets — what's in env vs hardcoded

rg -t ts -t js 'process\.env\.' src/ | head -20
# anything that looks like a baked-in key
rg -t ts -t js -P '(sk_live|sk_test|AIza|AKIA|ghp_)[A-Za-z0-9_-]+'

Build a one-page "what I see" summary. This is not the threat model yet. It's the raw map.

3 — Interview the user to fill gaps

The code can't tell you:

• Who's a threat? External attackers, malicious users, malicious tenants in a shared platform, compromised employees, supply-chain attackers, hostile nation-states, customers' angry exes (real for consumer apps), automated scrapers?
• What's actually sensitive? Code doesn't know "this column holds adoption records sealed by court order" vs "this column holds usernames".
• What's the blast radius if X is breached? Regulatory? Reputational? Existential?
• What's been deliberately accepted? "Yes we know the admin app trusts the network — we're inside the VPN and that's by design." Threats that have been considered and accepted are not findings.

Run the interview short and pointed (see interview-guide.md for the question bank). Cap it at 30 minutes — if there's a 90-minute discussion to be had, surface that as "needs follow-up" and don't try to do it inside the threat model.

4 — Map the four pillars

Now write the structured part of the doc:

• Assets: what's worth protecting. Be specific. "User PII" is lazy; "billing addresses, IP at signup time, support-chat transcripts" is real.
• Actors: who would attack and why. Internal vs external. Authenticated vs anonymous. Skilled (nation-state, organized fraud) vs opportunistic (a script kiddie hitting your signup endpoint).
• Entry points: every place untrusted data enters the system. Every. Place. HTTP, GraphQL, webhooks, file upload, OAuth callback, email parsing, push notification tokens, support-form attachments.
• Trust boundaries: where authority shifts. The browser ⇄ backend boundary. Backend ⇄ third-party API. Application ⇄ database. Tenant A ⇄ tenant B. Free user ⇄ paid user ⇄ admin.

Each pillar gets a short numbered list. No paragraphs. The structure exists to be queried.

5 — Walk the frame

For STRIDE: for each asset, for each relevant entry point, ask the six questions:

Skip questions that genuinely don't apply ("repudiation isn't relevant to this read-only public endpoint") and say so — silence reads as oversight, but an explicit "N/A: this endpoint is unauthenticated and stateless" reads as rigor.

See stride-reference.md for the full prompt bank per letter, including the modern web concerns STRIDE alone doesn't surface (SSRF, deserialization, subdomain takeover, OAuth scope creep, race conditions on state machines).

6 — Score and rank

For each threat:

• Likelihood: 1 (theoretical) → 5 (someone will try this in the first month)
• Impact: 1 (annoyance) → 5 (existential)
• Severity = likelihood × impact (1–25)
• Mitigation status: existing / partial / none

Sort by severity. Top 5 go into the "fix this sprint" list. The rest sit in the model as known risks with planned mitigations or accepted-risk notes.

Be honest about likelihood. A SQL injection in code that doesn't touch user input is likelihood 1 even if impact is 5. A missing rate limit on /login is likelihood 5 because someone is going to try it tomorrow.

7 — Capture assumptions and open questions

The model is only valid under specific assumptions. Write them down explicitly:

• "Assumes the admin app is only reachable from the corp VPN."
• "Assumes Stripe is the only PCI-handling component."
• "Assumes session tokens in cookies are sufficient for CSRF protection because of SameSite=Lax."

If any assumption flips later (admin app gets a public endpoint; you start handling PANs; you switch to a SPA on a different domain), the model needs a re-run. Note it.

Open questions go in their own section, each with an owner. "Who actually has access to the prod DB read-replica?" is a question for ops, not a guess for the model.

8 — Hand off

Done. The doc is the artifact. Now:

• Use it as input to security-sentinel — "scan with priority on the top-5 threats from THREAT_MODEL.md"
• Use it as input to api-architect for new endpoints — every new entry point inherits the boundary controls the model demands
• Re-run quarterly or when any assumption changes (whichever is sooner)

Patterns and anti-patterns

✅ Do:

• Threat-model the change, not the whole company. A model for "the new invitations feature" is more useful than a model for "all of our SaaS".
• Name assumptions explicitly. The doc's job is to be invalidated later; named assumptions make that easy.
• Include the things you aren't worried about and why. The absence of a concern reads as oversight; the explicit decision reads as rigor.
• Walk the model with the team after writing it. A model the team disagrees with is more useful than one they merely nod at.

❌ Don't:

• Don't write a generic threat model from a template. If yours could equally describe Slack and Stripe, it's describing nothing.
• Don't list 80 threats without ranking. Pasta isn't a model.
• Don't conflate threat (what attacker would do) with vulnerability (how the code lets them). Threat → goal; vulnerability → mechanism. The model is the goals; the scan finds the mechanisms.
• Don't model "an APT with unlimited budget" if you're a 5-person company shipping a SaaS. Pick threats proportional to who actually targets you.
• Don't make the model a one-time artifact. A model that hasn't been touched in a year is decoration.

Example invocation

User: "We're shipping an invitations feature next week. Threat-model it."

1. Frame: STRIDE for the invitation endpoints; abuser stories for the user-facing accept page.
2. Bootstrap: read src/api/invitations/ — 5 endpoints, JWT auth on 3, public + token-based on 2. Webhook handler for email-bounce notifications. Send-email goes through a third-party (SendGrid).
3. Interview (15 min with the user):
   • Sensitive assets: pending invitation tokens (one-time signup grant), email addresses (PII), inviter identity.
   • Actors of concern: random external attackers, malicious users trying to invite themselves to other orgs, scrapers harvesting valid emails.
   • Trust boundaries: browser ⇄ public-accept endpoint; backend ⇄ SendGrid webhook; tenant A ⇄ tenant B.
   • Accepted risks: "We don't try to prevent users from inviting fake emails — we expect bounce rates."
4. Pillars:
   • Assets: invitation token (high), org membership grant (high), email address (medium)
   • Actors: ext-anon, ext-authenticated-other-org, malicious-tenant, automated-scraper, sendgrid-compromise
   • Entry points: POST /invitations (auth), GET /invitations/by-token/:t (public+rl), POST /accept (public+token), POST /webhooks/sendgrid (public+sig)
   • Trust boundaries: 3 listed above
5. STRIDE walk — 14 threats identified, e.g.:
   • S Token-guessing on /by-token → likelihood 4, impact 4 (sev 16): need 128-bit token + rate limit
   • I Enumeration via "email already invited" error → likelihood 5, impact 2 (sev 10): return same response either way
   • R No audit log on accept → likelihood 3, impact 3 (sev 9): log to existing audit table
   • E Mass-assignment on invitation create lets a regular user set role: admin → likelihood 4, impact 5 (sev 20): strict allowlist in the handler
   • D No rate limit on /by-token enables 100k req/min crawl → likelihood 4, impact 3 (sev 12): rate-limit by IP and by token-prefix
   • T SendGrid webhook signature check is currently absent → likelihood 3, impact 4 (sev 12): verify signature with crypto.timingSafeEqual
6. Top-5 ranked for the sprint: mass-assignment, token-guessing, sendgrid signature, rate limit on /by-token, enumeration.
7. Assumptions: tokens are 128-bit random from crypto.randomBytes; SendGrid is the only path; accept endpoint always requires an authenticated session.
8. Open questions: do we want a "this invitation has been forwarded" detection? (privacy team to decide).
9. Output: docs/security/THREAT_MODEL-invitations.md with all of the above, and a structured front-matter block for security-sentinel to consume.

See also

• security-sentinel — runs the targeted scan informed by this model
• api-architect — sets the auth/boundary controls the model demands for new endpoints
• code-auditor — finds the specific bugs corresponding to the threats this model named
• resilience-engineer — many tampering/repudiation threats are closed by typed errors + structured logging
• Reference files: stride-reference.md, interview-guide.md

Compatibility

Works in both Claude Code (native SKILL.md auto-routing) and OpenAI Codex CLI (via the for-codex/AGENTS.md routing table). No code execution required — this is an interactive skill that reads files and asks questions; safe to run in any repo.

Attribution

The structural pattern (split planning from finding; bootstrap-from-code then interview; rank by likelihood × impact) is inspired by Anthropic's open-source defending-code-reference-harness, specifically how it separates a planning skill (/threat-model) from a finding skill (/vuln-scan). That repo is Apache 2.0, focused on C/C++ memory bugs.

This skill targets the React + Node.js / TypeScript stack the rest of fullstack-agent-skills is built for. Every workflow step, prompt, grep recipe, STRIDE prompt-bank, interview question, and example is written from first principles for web apps — JWT and OAuth pitfalls, IDOR and mass-assignment, SSRF and CORS, prototype pollution, Next.js getServerSideProps leaks, multi-tenant row-level guards. MIT licensed.