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

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

The symptom is usually obvious: an AI-built SaaS app is shipping requests from the browser with secrets in the client bundle, or Edge Functions are callable by anyone because auth was never enforced. The business impact is not subtle either. You get unauthorized usage, surprise bills, customer data exposure, broken trust, and a launch delay while you clean up the mess.

The most likely root cause is that the product was built fast with Supabase defaults and no security pass before deployment. The first thing I would inspect is the live frontend bundle and the Edge Function entry points, because that tells me whether secrets are already public and whether every sensitive route is actually checking identity before doing work.

Triage in the First Hour

1. Check the live site source and browser network calls.

• Look for any Supabase service role key, third-party API key, or internal endpoint URL in shipped JavaScript.
• Confirm whether requests are being made directly from the client instead of through a protected server path.

2. Review Supabase project settings.

• Verify which keys exist: anon key, service role key, and any custom JWT settings.
• Check Row Level Security status on every table that stores user data.

3. Inspect Edge Functions.

• List all deployed functions and confirm whether each one validates a bearer token.
• Check logs for unauthenticated hits, repeated failures, or abnormal traffic spikes.

4. Audit environment variables in deployment platforms.

• Check Vercel, Netlify, Cloudflare Pages, GitHub Actions, or whatever is used for deploys.
• Confirm secrets are not marked as public or bundled into client builds.

5. Review recent commits and build artifacts.

• Find the commit where auth was added late or skipped entirely.
• Search for hardcoded keys in source files, test files, sample env files, or generated code.

6. Check Supabase Auth and database policies.

• Confirm sign-in flow works end to end.
• Verify users cannot read or mutate another user's records through direct database access.

7. Inspect monitoring and billing signals.

• Look for unusual function invocations, database reads, storage access, or API spend.
• If abuse is active, rotate keys before anything else.

A simple diagnostic command I would use early:

grep -RInE "service_role|SUPABASE|sk_live|api_key|secret" .

If that returns anything inside frontend code or committed files, I treat it as a production incident until proven otherwise.

Root Causes

| Likely cause | What it looks like | How I confirm it | | --- | --- | --- | | Service role key exposed in frontend code | The app can call privileged actions from the browser | Search built assets and source maps for `service_role` or secret-like strings | | Missing auth check in Edge Functions | Any user can hit function URLs directly | Call logs show anonymous requests succeeding without a valid JWT | | RLS disabled on tables | Users can read or write data they should not see | Supabase table policies are absent or permissive | | Client-side direct access to privileged APIs | Browser talks straight to admin endpoints | Network tab shows calls that should only happen server-side | | Misconfigured environment variables | Secrets are stored as public build vars | Deploy dashboard shows secret values under public env scope | | Weak onboarding flow masks auth bugs | App appears to work until data isolation fails later | Test with two users and compare access to each other's records |

The biggest mistake founders make here is assuming "it works" means "it is safe". In this failure mode, working code often means broken security with a nice UI on top.

The Fix Plan

First, I would stop any active exposure. If a real secret has leaked, I rotate it immediately before touching application logic. That includes Supabase service role keys, third-party AI keys, email provider keys, webhook secrets, and anything else with write privileges.

Next, I separate public from private responsibilities.

• Keep only `anon`-level values in the client.
• Move privileged operations behind Edge Functions or another server boundary.
• Remove all direct client use of admin credentials.
• Replace any hardcoded secret with environment variables stored only on the server side.

Then I lock down authorization at three layers:

1. Authentication

• Require a valid signed-in session for every sensitive action.
• Reject requests with missing or expired tokens.

2. Authorization

• Verify the caller owns the record they are trying to read or update.
• Use user IDs from verified auth context only, never from request body alone.

3. Database policy

• Turn on RLS for all user-facing tables.
• Write explicit allow rules for select/insert/update/delete based on authenticated user ID.

For Supabase Edge Functions specifically, I would make auth mandatory at the top of each sensitive handler. The function should fail closed if identity cannot be verified.

const authHeader = req.headers.get("Authorization");
if (!authHeader?.startsWith("Bearer ")) {
  return new Response("Unauthorized", { status: 401 });
}

That alone is not enough by itself. After extracting the token check into shared middleware or helper logic, I would verify it against Supabase auth before allowing any database write or AI action.

Then I clean up deployment hygiene:

• Rotate leaked secrets everywhere they were used.
• Delete old preview deployments that may still contain stale env values.
• Regenerate source maps if they expose implementation details publicly.
• Ensure `.env`, `.env.local`, backup files, and build artifacts are excluded from git.
• Rebuild and redeploy from a clean commit after removing unsafe code paths.

If there are AI features involved, I also put guardrails around tool use:

• Do not let model output directly trigger privileged actions without validation.
• Do not pass raw user input into system prompts containing secrets.
• Sanitize retrieval sources so prompt injection cannot instruct the model to exfiltrate tokens or hidden context.
• Log high-risk tool calls for review.

My recommendation is to fix this in one tight sprint rather than patching it across multiple casual commits. Half-fixes create false confidence and usually leave one backdoor open.

Regression Tests Before Redeploy

Before shipping again, I would run tests that prove both security and behavior still work.

Acceptance criteria:

• Anonymous users get 401 or 403 on every protected Edge Function.
• Signed-in users can only access their own records.
• No secret appears in client bundle output or browser network traces.
• RLS blocks cross-user reads and writes at the database layer.
• All critical flows still work after rotation of leaked keys.

QA checks I would run:

1. Auth tests

• Try every protected endpoint without a token.
• Try with an expired token.
• Try with a valid token belonging to another user.

2. Data isolation tests

• Create two test accounts.
• Confirm account A cannot fetch account B's rows through UI or direct API calls.

3. Secret scanning

• Scan repo history if needed for leaked credentials still present in old commits.
• Scan built assets after deployment preview to ensure nothing sensitive ships publicly.

4. Functional regression

• Sign up, sign in, password reset if applicable, billing flow if applicable,

AI generation flow if applicable.

• Confirm errors are understandable instead of exposing internal details.

5. Abuse checks

• Send repeated invalid requests to confirm rate limiting or at least safe failure behavior.
• Confirm logs do not store full secrets or raw tokens.

I also want one clean staging deploy before production. If staging does not match prod config closely enough to test auth realistically, then staging is not useful enough to trust.

Prevention

The long-term fix is not just "add auth". It is building guardrails so this does not happen again when someone ships fast under pressure.

Security guardrails:

• Require RLS on every new table by default.
• Block merges if any secret-like string appears in frontend code.
• Add CI checks for exposed environment variables and missing auth middleware on protected routes.
• Rotate keys on a schedule and immediately after any incident suspicion.
• Use least privilege everywhere possible instead of service role access by default.

Code review guardrails:

• Every API route gets reviewed for authentication first, behavior second.
• Any change touching env vars gets checked against deployment scope rules.
• Any AI tool action gets reviewed for prompt injection risk and unsafe side effects.

Monitoring guardrails:

• Alert on spikes in Edge Function invocations from unknown sources.
• Alert on repeated 401s followed by successful writes from new IP ranges if that pattern matters for your product.
• Track p95 latency for protected endpoints so security checks do not quietly break performance later; I want critical routes under 300 ms p95 where possible after caching and query cleanup.

UX guardrails:

• Show clear signed-out states instead of silent failures that push users into retry loops.
• Make permission errors readable so support tickets do not balloon unnecessarily.
• Keep onboarding honest about what requires login versus what is public-facing.

Performance guardrails:

• Do not move everything behind heavy server logic just because it feels safer; protect only what needs protection.
• Cache static assets through Cloudflare correctly so security fixes do not destroy load times unnecessarily when launch traffic arrives.

When to Use Launch Ready

Use Launch Ready when you need me to turn a fragile AI-built app into something safe enough to launch publicly without gambling on hidden leaks or broken access control. It fits best when you already have a working prototype but need domain setup, email deliverability, Cloudflare hardening, SSL, deployment cleanup, secrets handling,

What Launch Ready includes:

• DNS setup and redirects
• Subdomains
• Cloudflare configuration
• SSL setup
• Caching and DDoS protection
• SPF/DKIM/DMARC email records
• Production deployment
• Environment variables and secret cleanup
• Uptime monitoring
• Handover checklist

What you should prepare before kickoff:

1. Access to your domain registrar 2. Access to your hosting platform 3. Supabase project access 4. List of all third-party APIs used by the app 5. Any current staging URL plus production goal URL 6. A short note on which pages require login and which do not

If your app already exposed API keys or shipped without auth boundaries, I would treat this as an urgent rescue sprint rather than normal maintenance, because every hour it stays live increases risk of abuse, support load, and cleanup cost later.

Delivery Map

References

1. Roadmap.sh Cyber Security: https://roadmap.sh/cyber-security 2. Roadmap.sh API Security Best Practices: https://roadmap.sh/api-security-best-practices 3. Roadmap.sh QA: https://roadmap.sh/qa 4. Supabase Security Docs: https://supabase.com/docs/guides/auth/row-level-security 5. Supabase Edge Functions Docs: https://supabase.com/docs/guides/functions

---

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.*