A valid session and a working login screen don't prove access is controlled. The five patterns that show up over and over in live SaaS apps, from changing an object ID to a service-role key reachable from the browser.

A JWT payload is encoded, not encrypted. Decoding it and checking a user ID isn't validation. Pinning the algorithm, verifying issuer and audience, and why signature checks still won't stop IDOR on their own.

Adding a tenant_id column doesn't isolate anything on its own. Every query, route, webhook, and export has to enforce the boundary. Where multi-tenant isolation actually breaks, and how to test it like an attacker would.

Severity is a business decision, not a scariness score. What each rating should actually mean, from an exposed service-role key at critical down to a missing header at low, and how to act on the report Monday morning.

A valid login session doesn't grant universal access. Where authorisation checks actually break in a fast-moving SaaS: client-side-only admin checks, stale JWT claims, and Supabase RLS that exists in theory but not in production.

A leaked key is active exposure, not a housekeeping task. The correct sequence: map every dependency, generate a scoped replacement, validate before revoking, then investigate what the key may have already allowed.

AI doesn't make an app insecure by default. It removes the natural pauses where a developer would question an assumption. Where AI-assisted apps actually fail, and the five checks worth running before launch.

Most API breaches aren't dramatic. They start with a predictable request, a missing ownership check, and data that was never meant to come back. The failures attackers test first, and how to check for them.

An audit tells you what's wrong. Remediation converts a finding into a verified outcome. What the fix actually looks like for exposed keys, broken RLS, IDOR, and payment logic.

Not every finding deserves the same urgency. What a tightly scoped 48 hour audit checks first: exposed credentials, IDOR, Supabase RLS, and payment trust boundaries, and how to prioritize what comes back.

An app can pass every happy-path test and still ship a service-role key in source or an IDOR route. Where Semgrep actually helps in a JavaScript codebase, and what a clean scan still can't prove.

Intercepting requests isn't the same as finding a vulnerability. What a real Burp Suite engagement tests: cross-tenant IDOR, JWT validation, Supabase RLS, and payment flows an automated scan can't verify alone.

A ZAP scan shows how an attacker sees your live app, not how the code was meant to behave. How to scope it safely, interpret the findings, and know what it can't tell you.

Running npm audit and clearing every warning isn't a review. What actually matters: whether the vulnerable version is deployed, whether the code path is reachable, and whether the fix breaks something else.

A missing header rarely looks urgent until it turns a small frontend mistake into account compromise. The headers that matter for live SaaS products, and the trade-offs that break a rushed rollout.

A successful test payment proves the happy path works. It doesn't prove a customer can't alter a price, replay a webhook, or claim someone else's purchase. Where Stripe integrations actually break.

Rate limiting alone won't stop credential stuffing. The layered defense that actually works: progressive delays, MFA on privileged accounts, hardened password reset, and the session controls most AI-built auth skips.

A public Supabase database is usually RLS disabled, a permissive policy copied from a tutorial, or an endpoint that trusts a user-supplied ID. How to find it, lock it down, and verify the fix holds.

RLS enabled doesn't mean your data is protected. The failures that actually expose customer records: disabled tables, write-only gaps, spoofable tenant IDs, and exposed service-role keys.

Learn how to test for IDOR vulnerabilities in a live SaaS app, confirm broken object access safely, and apply fixes that protect every customer record.

A deleted .env file isn't gone if it was ever committed. What to rotate first, how to rewrite history safely with git filter-repo, and why the cleanup matters less than the credential rotation.

API key leaks rarely come from someone deliberately publishing a secret. They come from rushed deploys, frontend variables, and old commits. Here's where to actually look, and how to triage what you find.

What a real security audit actually examines in an AI-built application, from exposed credentials and broken RLS policies to payment logic that trusts the browser instead of the server.

What IDOR actually means, a concrete example of how it works, why AI-generated code is particularly exposed to it, and how it gets caught during a security audit.

What broken access control actually means, what IDOR looks like in a vibe-coded app, and what an access control review checks for. In plain language, no technical background required.

Bolt.new Security: The Most Common Vulnerabilities in Bolt-Built Apps

The full Supabase security checklist covering RLS, service_role key exposure, storage permissions, auth configuration, and database functions. Ordered by severity.

RLS is just one layer. This covers the rest of the Supabase security surface: service_role key exposure, storage bucket permissions, auth configuration, and database function access.

What API keys are, how they leak, what the automated attack chain looks like, and what proper credential handling actually looks like for founders building with AI tools.

What security headers are, which ones matter most, and what actually happens when they're missing from your AI-built app. No technical background required.

A curated list of free security tools and research every AI founder should know before launch. From OWASP LLM Top 10 to live scanning tools, no technical background required.

83% of Supabase database exposures trace back to disabled Row Level Security. Here's what an unprotected table actually looks like to an attacker, including the Moltbook breach and CVE-2025-48757, plus a 30-second way to check your own app.

AI coding tools ship you speed and confidence, but the research shows 45% of AI generated code introduces OWASP Top 10 vulnerabilities, developers trust it more while it's measurably less secure, and the flaws are often structural.

I audited 8 Lovable apps in one week, and every single one had at least one high severity finding.

I have been auditing apps built with AI tools such as Cursor/Bolt/Lovable for quite some time now, and I have always noticed that the same security issues keep coming up in various different domains.

From what I’ve personally observed, most AI founders seem technical enough to bring their software products to life, but not quite technical enough to know what they are actually exposing when they use AI tools to build and ship quickly.