Production hardening for AI-generated software
AI App Hardening for
Vibe-Coded Software
Your AI builder made the demo. Now make it survive production. Stackbilder generates STRIDE threat models, ADRs, integration test plans, and governed scaffolds — before the codebase becomes a haunted house.
The problem with vibe coding
The demo works. The production system is a different story.
AI app builders — Lovable, Bolt, v0, Cursor, Claude Code, Replit — are genuinely impressive at one thing: getting to a working demo fast. They handle routing, components, and basic CRUD. They get you from idea to browser in hours.
What they don't generate: a threat model identifying your SQL injection vectors. An ADR documenting why you chose cookie sessions over JWTs. A test plan covering your Stripe webhook race conditions. Deployment constraints that prevent the next developer from accidentally disabling auth.
The problem isn't vibe coding. The problem is vibe shipping — pushing AI-generated code to production without the governance layer that makes it maintainable, auditable, and secure.
Stackbilder is the governance layer. Run it before you ship.
What Stackbilder generates
Four governance artifacts. One scaffold. Zero hallucinations.
Threat Model
STRIDE-based security analysis specific to your architecture. Identifies spoofing vectors, tamper risks, repudiation gaps, information disclosure paths, availability threats, and privilege escalation scenarios — before you write the first line of application code.
JWT algorithm confusion attack via alg:none bypass.
Severity: CRITICAL | Mitigation: strict alg allowlist
## T-004 Information Disclosure
D1 error messages expose schema via verbose SQLite errors.
Severity: MEDIUM | Mitigation: sanitize error responses
Architectural Decision Records
Every non-obvious architectural choice documented with context, alternatives considered, and consequences. ADRs prevent the next developer (or AI agent) from undoing decisions that have non-obvious reasons. Your architecture becomes self-explaining.
Decision: Cookie-based sessions over stateless JWTs.
Context: Edge runtime + D1 session store available.
Consequence: Session revocation is immediate. Token refresh loop eliminated.
Test Plan
Integration and unit test specifications derived from your architecture. Covers auth flows, billing webhook scenarios, data access patterns, and error conditions. Not boilerplate — actual test cases for your specific app pattern.
test_auth_flow: login → session → protected route
test_billing: checkout → webhook → tier upgrade
test_rls: tenant_A cannot read tenant_B rows
Coverage target: 85% | Framework: vitest
Governed Scaffold
Project file structure with architectural constraints baked in. Machine-readable guardrails that prevent drift when AI agents modify your code later. The scaffold is the production starting point — not a prototype.
src/middleware/auth.ts
src/routes/api/{...}.ts
.ai/threat-model.md
.ai/adr-001-auth.md
.ai/test-plan.md
.ai/constraints.yaml
Who uses Stackbilder
Built for builders who ship with AI — and need to survive production.
Technical founders
Using Lovable, Bolt, or v0 to build your SaaS MVP. You need to get Stripe live, auth working, and real users in — without your app becoming a security incident on HackerNews.
Agencies with AI tooling
Shipping client MVPs with AI tools. Your clients expect production-grade deliverables. Stackbilder gives you the governance documentation that justifies your rate.
Cloudflare Workers devs
Building on the edge with D1, KV, R2, and Durable Objects. Stackbilder generates Cloudflare-native scaffolds with bindings, auth, and D1 patterns wired up correctly from day one.
Cursor and Claude Code users
AI coding agents move fast and make confident, unsubstantiated choices. Stackbilder gives your agents constraints, threat models, and documented decisions so they work within your architecture.
Security-conscious builders
You want threat models, ADRs, and test plans but don't have time to write them manually. Stackbilder generates them deterministically — no token roulette, no hallucinated security advice.
Small teams without a platform team
Backstage is great if you have 3 engineers to run it. Stackbilder delivers the same governance output with zero operational overhead — no YAML files to maintain, no portal to run.
How it works
Describe your app. Get production architecture in 20ms.
Describe your intention
Type what you want to build in plain language. "A SaaS with Stripe billing and multi-tenant D1 data isolation" is enough.
Deterministic scaffold
The TarotScript engine maps your intention to an architectural pattern and generates your governance suite in ~20ms. No inference, no hallucinations.
Ship with governance
Your threat model, ADRs, test plan, and scaffold are ready to download. Use them as a blueprint regardless of what AI tool you build with.
Common questions
What is AI app hardening?
AI app hardening is the process of adding production-grade security analysis, architectural documentation, and test specifications to code generated by AI tools. AI builders create functional prototypes quickly but skip the governance layer that real production systems require: threat models, ADRs, test plans, and deployment constraints.
My app is already built. Is it too late?
No. Stackbilder generates governance output based on your app's intention and architectural pattern — not its implementation. You can run it at any point, then use the threat model and test plan to audit your existing codebase. Many teams use Stackbilder after a Lovable or Bolt build to understand what they need to harden before going live.
Does this replace security review?
Stackbilder automates the documentation layer — threat models, ADRs, test specifications — which is foundational for security review. It does not replace a professional penetration test for high-risk applications. For teams that need deeper review, our consulting offering includes bespoke security engagements.
Is the output the same every time?
Yes. The core scaffold engine is deterministic — same intention produces the same architectural output every time. This is a feature, not a limitation. You can trust the threat model won't change between runs. Pro adds optional LLM polish that generates idiomatic implementation code, guided by the deterministic constraints.
What stacks does Stackbilder support?
Cloudflare Workers (D1, KV, R2, Durable Objects, bindings, edge auth) is fully supported today. Additional stacks — Vercel/Next.js, Supabase, AWS Lambda — are on the roadmap.
Related pages
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