Borrowing it
Nothing to install: this file belongs to Srajangpt1/ai-security-crew. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Srajangpt1/ai-security-crew/main/.claude/commands/threat-model.mdgit clone --depth 1 https://github.com/Srajangpt1/ai-security-crewWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/srajangpt1/ai-security-crew/threat-model)<a href="https://agentmods.dev/commands/srajangpt1/ai-security-crew/threat-model"><img src="https://agentmods.dev/badge/commands/srajangpt1/ai-security-crew/threat-model/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/srajangpt1/ai-security-crew/threat-model"><img src="https://agentmods.dev/badge/commands/srajangpt1/ai-security-crew/threat-model.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.01241 |
| Opus 5 | $0.00000 | $0.00620 |
| Sonnet 5 | $0.00000 | $0.00248 |
| Haiku 4.5 | $0.00000 | $0.00124 |
Grade A, and why
threat-model scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Perform a threat model for the following feature or component:
$ARGUMENTS
Instructions
Produce a developer-focused threat model for the feature or component described above. If no description is provided, ask the user:
- What are they building? (feature name + description)
- What tech stack is involved?
- Are there code snippets, data flows, or architecture notes to analyze?
Write threats in plain language — describe concrete attack scenarios a developer would understand, not abstract STRIDE categories. Every threat must link to evidence from the artifacts provided.
Step 1 — Understand What We're Building
Extract from the input:
- Feature/component name — what is this thing?
- Description — what does it do, what problem does it solve?
- Tech stack — languages, frameworks, databases, cloud services
- Data touched — what data flows through this feature? (credentials, PII, payment data, tokens, internal config, etc.)
- System boundaries — what calls this? what does it call? external vs internal?
- Architecture notes — deployment model, trust boundaries, network zones
Step 2 — Identify Security Signals
Scan the description and any provided artifacts for:
Attack surfaces:
- Authentication endpoints (login, registration, password reset, OAuth)
- File upload / download handlers
- External API integrations (third-party services, webhooks)
- Admin / privileged operations
- Data exports or bulk operations
- Cross-tenant operations in multi-tenant systems
- Unauthenticated or public endpoints
Sensitive data patterns:
- Credentials and secrets (passwords, API keys, tokens)
- PII (names, emails, phone numbers, addresses)
- Financial data (card numbers, account numbers, transactions)
- Health data (PHI, medical records)
- Internal configuration or infrastructure details
Technology-specific risks:
- JWT: algorithm confusion, none-algorithm, token theft, refresh token abuse
- OAuth: redirect URI manipulation, CSRF on callback, token leakage in logs
- SQL databases: injection, excessive permissions, unencrypted sensitive columns
- File handling: path traversal, unrestricted upload, SSRF via URL fetch
- Redis/caching: cache poisoning, insecure TTLs, unauthenticated access
- Microservices: service-to-service auth, internal SSRF, broken trust boundaries
- Webhooks: signature validation, replay attacks, SSRF via callback URLs
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 137 lines · 0 tokens per session scan A cad8544470d6
threat-model is a command published in the GitHub repository Srajangpt1/ai-security-crew (68 stars, last pushed 4mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,241 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.