How I Would Fix exposed API keys and missing auth in a Lovable plus Supabase AI-built SaaS app Using Launch Ready.

How I Would Fix exposed API keys and missing auth in a Lovable plus Supabase AI-built SaaS app Using Launch Ready

The symptom is usually blunt: a founder discovers their Supabase anon key, service role key, or third-party API key is visible in the browser bundle, a public repo, or a Lovable-generated config file, and the app also lets users hit protected actions without real authorization. In business terms, that means data exposure risk, surprise usage bills, broken onboarding trust, and a support mess if someone can read or write records they should never touch.

The most likely root cause is that the app was assembled fast with AI tools, but security boundaries were never designed. The first thing I would inspect is the live request path: what the browser can call directly, which Supabase tables are exposed through RLS, where secrets are stored, and whether any privileged key is present in client-side code or build output.

Triage in the First Hour

1. Check the live site in an incognito browser.

• Open DevTools and inspect Network requests.
• Confirm whether sensitive endpoints are called directly from the client.
• Look for any request using a service role key, admin token, or third-party secret.

2. Inspect the deployed environment variables.

• Review Vercel, Netlify, Cloudflare Pages, or the host used by Lovable exports.
• Verify that only public-safe values are exposed to the client.
• Confirm that all private keys are server-only.

3. Review Supabase settings.

• Check Auth providers, JWT settings, Row Level Security status, and table policies.
• Confirm whether critical tables have RLS enabled.
• Verify if anonymous access is unintentionally allowed.

4. Audit recent builds and commits.

• Search for `SUPABASE_SERVICE_ROLE_KEY`, Stripe secrets, OpenAI keys, webhook secrets, and any `.env` leakage.
• Check whether AI-generated code hardcoded credentials into components or utility files.

5. Inspect logs and usage spikes.

• Look at Supabase logs, hosting logs, and third-party dashboards.
• Watch for unusual traffic, repeated unauthorized requests, or sudden token usage.
• If there is evidence of exposure, rotate immediately before continuing.

6. Test login and protected flows manually.

• Try accessing dashboard pages while logged out.
• Try direct API calls to write records as another user.
• Confirm whether auth checks exist on both UI routes and backend actions.

7. Freeze changes for one short window.

• Stop new deploys until you know what is public and what is private.
• This avoids patching one leak while another build reintroduces it.

## Quick scan for obvious secret leaks
grep -RInE "service_role|sk_live|secret|api[_-]?key|SUPABASE_" .

Root Causes

1. Secrets were placed in client-side environment variables.

• Confirmation: inspect bundled JavaScript or frontend env usage like `VITE_`, `NEXT_PUBLIC_`, or similar public prefixes containing private values.
• If a secret appears in browser code or source maps, it is already compromised.

2. Supabase Row Level Security was left off on important tables.

• Confirmation: check each table policy in Supabase and verify whether unauthenticated users can select, insert, update, or delete rows.
• If RLS is disabled on customer data tables, the app may be wide open even if login exists.

3. Auth was added only in the UI layer.

• Confirmation: routes may hide buttons when logged out but backend functions still accept requests without verifying session claims.
• If an attacker can call the endpoint directly with no session check, the protection is cosmetic only.

4. Lovable generated code with unsafe defaults during fast iteration.

• Confirmation: search for broad CORS settings, permissive fetch calls, direct admin SDK use in components, or placeholder auth logic never replaced with real checks.
• AI builders often optimize for demo flow first and security second.

5. Service role access was used where anon access should have been scoped down.

• Confirmation: look for serverless functions or edge functions using privileged keys for ordinary user actions like profile updates or content reads.
• If one key can do everything, one leak becomes a full compromise.

6. Redirects or subdomain handling exposed internal routes unintentionally.

• Confirmation: test old preview URLs, staging subdomains, and forgotten deployment links from before launch.
• A stale preview environment often keeps working after people assume it is dead.

The Fix Plan

My approach would be to contain first, then repair permissions, then redeploy cleanly. I would not try to "patch" this by hiding buttons or adding a quick client-side check because that only reduces noise; it does not stop abuse.

1. Rotate every exposed secret immediately.

• Regenerate Supabase keys if any privileged key was leaked.
• Rotate third-party API keys that appeared in code or logs.
• Revoke old tokens so leaked copies stop working.

2. Move all private secrets out of the client bundle.

• Keep public values only where they are meant to be public.
• Put sensitive credentials in server-side env vars only.
• Remove secrets from any Lovable-generated frontend files that get shipped to browsers.

3. Turn on RLS for every table holding user data.

• Add policies for read/write access based on authenticated user ID or org membership.
• Deny by default unless there is a clear business rule allowing access.

4. Add server-side authorization checks on every protected action.

• Verify session identity before reading or writing data.
• Check ownership at the record level before updates or deletes.
• For admin actions, require explicit elevated roles instead of trusting frontend state.

5. Split public and privileged operations cleanly.

• Public reads should use anon-safe queries with strict policies.
• Privileged writes should go through server routes or edge functions with validation and audit logging.

6. Lock down CORS and webhook verification if applicable.

• Allow only approved origins needed by the product surface area.

| Use case | Safe pattern | | --- | --- | | Browser reads | anon key + RLS | | User writes | authenticated session + RLS | | Admin tasks | server-only route + service role | | Webhooks | signature verification + replay protection |

7. Clean up deployment artifacts after rotation.

• Purge caches if sensitive content may have been stored at the edge.

Cache invalidation matters here because old bundles can keep exposing old values after you think you fixed them.

8. Add basic monitoring before re-opening traffic fully . Set alerts on auth failures, unusual request volume, failed webhook signatures, and spikes in API usage costs.

If I were delivering this as Launch Ready work, I would make sure domain, email, Cloudflare, SSL, deployment, secrets,

because fixing auth without fixing deployment hygiene usually leaves another hole open somewhere else.

Regression Tests Before Redeploy

I would not ship until these checks pass:

1. Anonymous access tests - Try every protected page while logged out .

• Expected:

No customer data loads, no write actions succeed, and no hidden admin routes respond with useful content .

2. Authorization tests - Log in as User A, then try to access User B's records by ID .

• Expected:

403, 404, or empty response depending on design, but never data from another account .

3. Secret exposure tests - Search built assets, source maps, and runtime configs for private keys .

• Expected:

No service role keys, no third-party secrets, and no internal URLs embedded in client bundles .

4. Supabase policy tests - Verify each critical table has RLS enabled .

• Expected:

Policies match intended roles, and unauthenticated users cannot read or mutate protected rows .

5. Login flow tests - Test sign up, sign in, password reset, session refresh, and logout .

• Expected:

Sessions persist correctly, expired sessions fail safely, and redirects land on valid pages .

6. Error handling tests - Force invalid tokens, expired sessions, and missing required fields .

• Expected:

Clear error messages for users, no stack traces exposed, and no silent partial writes .

7. Basic performance sanity check - Confirm auth checks do not add heavy latency to normal flows .

• Expected:

Protected page load stays under 2 seconds p95 on decent mobile networks; dashboard interactions remain responsive with no obvious UI lag .

Acceptance criteria I would use:

• Zero private keys present in any client-visible asset
• All customer tables have RLS enabled
• Every protected endpoint rejects unauthenticated requests
• No cross-account data access in manual tests
• Monitoring alerts active before launch

Prevention

The fix should not depend on heroics next time. I would put guardrails around security reviews so this does not come back after the next AI-assisted feature sprint.

1. Add a release checklist for every deploy. - Confirm env vars are split into public vs private buckets .

• Confirm RLS policies exist for new tables

.

• Confirm auth checks exist on new routes

2. Require code review focused on behavior first

not style

.

• I care about who can read what ,

who can write what , and whether secrets ever reach the browser

3 . Keep least privilege as default

.

• Use anon keys only where safe

• Use service role keys only inside trusted server contexts

4 . Monitor security signals continuously

.

• Alert on unusual auth failures ,

unexpected API spend , new preview deployments , and edge errors

5 . Red team common AI-builder mistakes

.

• Prompt injection into AI features

• Data exfiltration through chat tools

• Unsafe tool calls from model output

• Missing human approval on destructive actions

6 . Improve UX around auth states

.

• Show loading ,

empty , error , and expired-session states clearly

• Do not let users think an action succeeded when it failed silently

7 . Keep observability simple but real

.

• Log who did what ,

when , and from which route

• Track p95 latency ,

error rate , and rejected requests per release

When to Use Launch Ready

I would use Launch Ready when the product works well enough to keep building , but it is not safe to put money behind yet . This sprint fits if you need domain setup , email deliverability , Cloudflare protection , SSL , production deployment , secrets cleanup , uptime monitoring , and handover done fast without dragging this into a two-week architecture rewrite .

- You already have a working Lovable plus Supabase app

- You need launch safety more than new features

- You want DNS , redirects , subdomains , caching , DDoS protection , SPF / DKIM / DMARC , environment variables , and monitoring handled together

- You need one senior engineer to reduce launch risk instead of five freelancers making partial fixes

What I would ask you to prepare: - Hosting access

- Domain registrar access

- Supabase project access

- Any third-party API dashboards

- A list of current roles : admin , user , guest , and anything custom

- A short note on which pages must stay public versus authenticated

If your app has exposed secrets plus missing auth , I would treat that as a launch blocker until containment , rotation , policy fixes , and regression tests are complete .

Delivery Map

References

1 . Roadmap.sh API Security Best Practices https://roadmap.sh/api-security-best-practices

2 . Roadmap.sh Code Review Best Practices https://roadmap.sh/code-review-best-practices

3 . Supabase Row Level Security docs https://supabase.com/docs/guides/database/postgres/row-level-security

4 . Supabase Auth docs https://supabase.com/docs/guides/auth

5 . OWASP API Security Top 10 https://owasp.org/www-project-api-security/

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Take the next step

If this is a problem in your product right now, here is what to do next:

• [Use the free Cyprian tools](/tools) - estimate cost, score app risk, check launch readiness, or pick the right service sprint.

• [Book a discovery call](/contact) - I will tell you honestly whether you need a sprint or if you can DIY the next step.

*Written by Cyprian Tinashe Aarons - senior full-stack and AI engineer helping founders rescue, launch, automate, and scale AI-built products.*