Lovable + Stackbilder · Production hardening
Lovable App Hardening:
Security, Tests, ADRs, and Architecture
Lovable gets your idea on screen in minutes. Stackbilder adds the threat model, auth review, database access patterns, test plan, and deployment guardrails before real users arrive.
What Lovable is great at
Lovable ships polished full-stack apps faster than almost anything else.
Lovable's core strength is translating a product description into a working full-stack application — React frontend, Supabase backend with Postgres, RLS, and auth, deployed to Vercel — with a quality of UI that takes skilled engineers days to achieve manually. For founders validating ideas, agencies building client MVPs, and builders iterating on product concepts, Lovable is a genuine force multiplier.
The generated code is not throwaway. Lovable produces real React components, working Supabase auth flows, and functional CRUD interfaces. The business logic is often surprisingly solid. Where the output falls short isn't in what it generates — it's in what it doesn't generate: the security analysis, architectural documentation, and test specifications that make a codebase maintainable and auditable once real users are in it.
The production gap
What production requires that Lovable doesn't generate
Lovable optimizes for speed to demo. These are the artifacts that don't ship with it — and that production systems require before real users land on your app.
Supabase RLS bypass threat model
Lovable enables RLS on core tables but doesn't document the anon key exposure risk or what happens when a new table is added mid-iteration without a policy. T-001 and T-002 are the two most common production incidents in Lovable apps.
Auth strategy ADR
Supabase Auth vs. a custom solution, session tokens vs. JWTs, refresh token rotation — these are decisions with long-term architectural consequences. Lovable picks one; Stackbilder documents why and what the alternatives were.
Multi-tenant isolation test plan
If your app serves multiple users or organizations, RLS is the isolation boundary. The test plan needs explicit coverage: tenant A must not be able to read tenant B's rows under any query path. This isn't boilerplate — it needs to map to your schema.
Stripe webhook security
Lovable will scaffold a Stripe integration, but replay attack protection — verifying the webhook signature before processing the event — is rarely documented. Missing this is a financial and data-integrity risk.
Deployment constraints
The Supabase anon key is a publishable key by design, but it should never grant meaningful access without RLS. Lovable doesn't generate a constraint document explaining this to the next developer or AI agent.
Storage bucket access policy
Supabase Storage buckets created during Lovable iterations can default to public. If your app stores user files, the access policy needs to be explicit — and documented as a constraint before you go live.
What Stackbilder adds
Four governance artifacts, specific to your Lovable stack.
Supabase Threat Model
STRIDE analysis tuned to the Supabase + Vercel deployment pattern. Covers anon key exposure, RLS bypass on new tables, auth token leakage, Storage bucket misconfig, and Stripe webhook replay — the five threats that actually end Lovable apps.
Supabase anon key in client bundle. Full table access
if any table lacks RLS policy.
Severity: CRITICAL | Mitigation: RLS required on all tables
## T-003 Tampering
Stripe webhook without signature verification.
Severity: HIGH | Mitigation: verify stripe-signature header
Auth Strategy ADRs
Every auth decision Lovable made — Supabase Auth vs. custom, session handling, JWT configuration — documented with context and consequences. Future you and future AI agents will know why the pattern is what it is, and why changing it carries risk.
Decision: Supabase Auth (not custom).
Context: Built-in RLS integration. MFA available.
Consequence: Auth-to-DB policy binding is automatic.
Reverting requires re-implementing session-to-policy bridge.
Multi-Tenant Test Plan
Test specifications covering RLS isolation between tenants, auth flow edge cases, Stripe billing lifecycle, and Storage access boundaries. Mapped to your schema — not generic auth test boilerplate.
test_rls: user_A cannot SELECT user_B rows via anon key
test_rls: new tables automatically get RLS policy
test_storage: bucket is private; signed URL required
Coverage target: 90% RLS paths | Framework: vitest
Governed Scaffold
Project structure with constraint files that document the RLS rules, Stripe webhook requirements, and Storage access policy. Machine-readable so AI agents working in your codebase later can't accidentally violate documented constraints.
src/api/webhooks/stripe.ts
supabase/migrations/
.ai/threat-model.md
.ai/adr-001-auth-provider.md
.ai/adr-002-rls-policy.md
.ai/constraints.yaml
Example output
Threat model for a Lovable SaaS with Supabase + Stripe
# Stackbilder Threat Model — Lovable/Supabase SaaS
Pattern: full-stack-supabase | Generated: deterministic
## Spoofing
T-001: Supabase anon key in client bundle.
Risk: Any table without RLS is publicly readable/writable.
Severity: CRITICAL
Mitigation: Enable RLS on all tables. Policy must reference auth.uid().
## Tampering
T-002: New table added without RLS policy during iteration.
Risk: Entire table contents accessible via anon key.
Severity: HIGH
Mitigation: Migration linter check; CI test_rls_coverage required.
## Tampering (Billing)
T-003: Stripe webhook without stripe-signature verification.
Risk: Replay attack escalates arbitrary user tier.
Severity: HIGH
Mitigation: Verify stripe-signature header before processing.
## Elevation of Privilege
T-004: Client-side auth gate without DB-layer RLS enforcement.
Risk: Direct API call bypasses UI check, accesses protected data.
Severity: CRITICAL
Mitigation: RLS is the only valid enforcement boundary.
## Information Disclosure
T-005: Supabase Storage bucket created as public.
Risk: User-uploaded files accessible without authentication.
Severity: HIGH
Mitigation: Default bucket policy = private. Signed URLs for delivery.
How they work together
Lovable builds the app. Stackbilder builds the governance layer.
These tools address different parts of the problem. Lovable answers "how do I get a working product in front of users fast?" Stackbilder answers "what does this product need to be safe, auditable, and maintainable once those users are in it?" Neither question invalidates the other.
The recommended workflow: run Stackbilder first, before you start a Lovable session. Use the threat model and architectural constraints as prompting context — paste the ADRs into your Lovable conversation to guide its decisions. The scaffold gives Lovable a governed starting point. If you're working with an existing Lovable build, the threat model becomes an audit checklist: work through each threat entry and verify the mitigation is in place.
Lovable is not a risk. Vibe shipping without the governance layer is the risk. Run both.
Common questions
Does Stackbilder work with Supabase specifically?
Yes. Supabase is one of the most common backends in Lovable-generated apps and has its own distinct threat profile. Stackbilder's Supabase pattern covers anon key exposure, RLS policy gaps on newly created tables, Storage bucket misconfigurations, and auth strategy decisions. The generated threat model and ADRs are specific to the Supabase architecture, not generic web app advice.
Can I run Stackbilder after I've already built in Lovable?
Yes. Stackbilder generates governance output from your app's intention and architectural pattern — not its implementation. Describe what you built, and you'll get a threat model, ADRs, and test plan you can use to audit and harden what already exists. Most teams run Stackbilder first to establish the governance baseline, but running it after a Lovable build works too.
Will Stackbilder tell me if my Supabase RLS is wrong?
Stackbilder generates the threat model identifying RLS bypass as a risk category — specifically T-001 (anon key exposure) and T-002 (missing policies on new tables). It documents what correct RLS coverage looks like for your data model and what tests to write to verify isolation. It does not directly inspect your Supabase project. Use the threat model as the diagnostic checklist.
Is Lovable good for production apps or just MVPs?
Lovable can produce code that goes to production, and many teams ship Lovable-generated apps to real users. The limitation isn't the code quality — it's the absence of the governance layer. A Lovable app with documented RLS policies, a Stripe webhook security review, and multi-tenant isolation tests is a production app. Without that layer, it's a well-built prototype with unknown risk.
What's the most common security gap in Lovable apps?
Supabase RLS gaps on tables added after the initial build. Lovable's initial scaffold often enables RLS on core tables, but as features get added in iterations, new tables can be created without policies. The anon key is in the client bundle, so any table without RLS is effectively public. This is T-002 in every Lovable threat model we generate.
Related pages
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