Works out what could go wrong in a system's design before it is built — who would attack it, how, and which defences are worth having. Use this whenever the user is designing a system that handles sensitive data, money, or authentication, asks what the security risks are, mentions threat modeling or STRIDE, or is starting a feature with a meaningful attack surface. This is design-time and systematic; for finding vulnerabilities in code that exists, use security-analysis.
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
# Threat modeling
Threat modeling asks *what could go wrong here* while the answer is still cheap to act on. Code
review finds the bug you wrote; threat modeling finds the defence you never designed.
Four questions structure the whole exercise:
1. **What are we building?**
2. **What can go wrong?**
3. **What are we going to do about it?**
4. **Did we do a good enough job?**
The output is a short list of decisions, not a risk register nobody reads.
## 1. Draw the system and mark the trust boundaries
You cannot reason about attacks without knowing where untrusted things meet trusted things.
Sketch: the components, the data flows between them, the datastores, and — most importantly —
the **trust boundaries** those flows cross. A boundary is anywhere the level of trust changes:
internet to your edge, your service to a third party, one tenant's data to another's, user to
admin.
Keep it to one diagram someone can hold in their head. See `diagramming`.
**Done when:** every trust boundary in the design is marked.
## 2. Name the attackers and what they want
Generic "hackers" produces generic mitigations. Be specific about who and why:
…
The line at the top of this page pins the label git:20260828.866dec6, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
A 17 of 17 checks passed. Fixed automatic checks with no AI involved, so the same checks give the same answer every run.
pass: Frontmatter block present
pass: Frontmatter declares a name
pass: Frontmatter declares a description
pass: Size between 200 bytes and 200 KB (5319 bytes)
pass: No zero-width or bidi control characters
pass: No instruction hidden inside an HTML comment
pass: No link to an exfiltration or paste host
pass: No credential-shaped string
pass: No instruction to send local credentials anywhere
pass: No text hidden with inline styles
pass: No prompt-injection phrasing
pass: No curl or wget piped into a shell
pass: No recursive delete of root, home or parent
pass: No instruction to read or print local credentials
pass: No base64 blob over 200 characters
pass: No link to a raw IP address
pass: No script tag
Source
GitHub
okhp3/skillz · 3 stars · license MIT · pushed 2026-11-06 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_gjwoyaz3d4mflest
GET https://markdownregistry.com/api/v1/resolve?ref=okhp3/skillz/threat-modeling
GET https://markdownregistry.com/api/v1/blob/c5055b21e564afbff151d3da95a23aba94e29f5a2e971eb9190a3e15d457aab8
Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.
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