Security: the gaps that don't show up in a demo
AI builders optimize for a working happy path. Authorization, input validation, and secret handling are the parts a generated app most often gets wrong, because nothing in the prompt-to-preview loop forces them to be right.
- Database access rules
Generated backends frequently ship with permissive or default row-level rules, so any authenticated user can read or write rows that aren't theirs. Verify every table enforces ownership, not just authentication.
- Exposed secrets and keys
API keys and service credentials sometimes land in client-side code or committed env files. Anything reachable from the browser bundle is public - rotate it and move it server-side.
- Missing server-side validation
Client-side checks are a UX nicety, not a security control. Confirm that inputs are validated and authorized on the server, where a user can't bypass them.
- Broken access control on endpoints
Generated API routes often check that you are logged in but not whether you are allowed to touch this specific record. That's the most common real-world breach class.
Scalability: what falls over at 1,000 users
A generated app feels instant with one user and a near-empty database. The patterns that quietly turn into outages only appear under real data volume and concurrency - and they are deterministic, so you can find them before they bite.
- N+1 queries
Rendering a list that fires one query per row is fine with 10 rows and catastrophic with 10,000. Look for loops that touch the database.
- Unbounded queries
Fetching an entire table with no pagination or limit works until the table is large, then it exhausts memory and times out.
- Missing indexes
Filters and sorts on unindexed columns trigger full-table scans that degrade smoothly into unusable as data grows.
Cost: the bill nobody modeled
Generated apps wire up managed services with default settings, which is convenient and occasionally expensive. The two failure modes are an architecture that bills per-operation in a hot path, and an AI feature with no caching or rate limiting.
Before launch, trace the most-hit user action through to the services it calls and ask what it costs at 100 and 10,000 active users. A single un-cached AI call or per-request third-party API in a high-traffic path is usually where margin disappears.
The pre-launch checklist
- Enforce row-level ownership on every table
Authentication is not authorization. Each query must prove the row belongs to the requester.
- Move every secret server-side and rotate exposed ones
Assume anything in the client bundle is already public.
- Validate and authorize on the server
Re-check inputs and permissions where the user cannot bypass them.
- Add pagination and limits to list queries
No endpoint should be able to return an unbounded result set.
- Index the columns you filter and sort on
Kill full-table scans before they become slow pages.
- Model the cost of your hottest user action
Price the busiest path at real user volume, including AI and third-party calls.
- Pin and audit dependencies
Confirm every package is real and intentional - hallucinated packages are a live attack vector.
Run this checklist on your repo, automatically
PeakStack scores every commit for security, scalability, and cost - with the exact line and a fix.
Request accessFAQ
Is Lovable safe to use for a real product?
Yes, with a review pass. Lovable is a fast way to build, but like any code it needs a security, scalability, and cost review before launch. The risk isn't the tool - it's shipping generated code unreviewed.
What is the most common security issue in Lovable apps?
Broken access control: endpoints and database rules that check whether you are logged in but not whether you own the specific record you are reading or writing. Fixing ownership checks closes the largest class of real breaches.
How do I check a Lovable app before launching?
Review the four dimensions that break products in production - security (access control, secrets, validation), scalability (N+1 queries, unbounded reads, indexes), cost (per-operation hot paths), and overall readiness. PeakStack scores all four automatically on every commit.