AI-Built App Security Test
Security testing for applications built with AI coding tools. Your app does what you asked for. We find out what else it does.
What is an AI-built app security test?
AI coding tools have changed who builds software, and how fast. With Lovable, Bolt, Replit, v0, Cursor, Claude Code or GitHub Copilot, an idea can become a working application in days, often by describing what it should do rather than writing every line. Some call it vibe coding, others AI-assisted development. The result is the same: a lot of code that no one has reviewed with security in mind.
An AI-built app security test is a security assessment designed for exactly these applications. We test the running app, review the code the AI wrote and check the backend services it is connected to, focusing on the mistakes AI tools make most often. You get clear findings, fixes you can apply with the same tool that built the app, and a retest to confirm they worked.
Why AI-built apps need testing
AI tools are built to make things work. Whether they are also secure is a question the tool rarely asks, unless you do.
The generated code looks professional and the app behaves exactly as intended in every demo, which is what makes the gaps so hard to spot. A missing permission check looks identical to a working one, until someone changes a number in a request and sees another customer's data.
Many of the people building these apps are not security specialists, and they shouldn't have to be. But once real users, payments or personal data are involved, you are responsible for protecting them, including under the GDPR. An independent test shows you where you stand before a customer, an investor or an attacker finds out for you.
What we typically find
The weaknesses in AI-built apps are rarely exotic. They are the predictable result of building fast without a security review, which is exactly why we know where to look:
- Broken access control: users who can read or change other users' data by changing an ID in the address bar or a request
- Open databases: Supabase tables without row-level security, or Firebase rules that let anyone read and write
- Exposed secrets: API keys, service keys and tokens shipped in the frontend code or committed to the repository
- Security that only runs in the browser: paid features, admin functions and input checks that anyone can bypass with the developer tools
- Risky dependencies: outdated packages with known vulnerabilities, and package names the AI invented that attackers may have registered
- Leftovers in production: debug output, test endpoints, sample accounts and admin panels that were never meant to go live
- Your own AI features: prompt injection, data leaking through the model, and API usage without limits or cost controls
What we test
Every test covers three layers, because in an AI-built app the weak spot is often where they meet:
The live application
We attack the running app from the outside and as a logged-in user in every role, the way a curious customer or a real attacker would. This shows what can actually be exploited, not just what looks suspicious in the code.
The source code
With read access to your repository, we review what the AI wrote: authentication and authorisation logic, the handling of secrets and user input, and the dependencies it pulled in. A code review finds problems that a test from the outside can miss.
The backend and its configuration
We check the services your app relies on: databases and their access rules, storage buckets, serverless functions, authentication providers and third-party APIs. This is where a single setting can expose all of your data at once.
How we work
We start with a short intake: what the app does, who uses it, what data it handles and which tools and services it was built with. You give us access to the app, a test account for each user role and, ideally, read access to the repository.
Where possible we test on a staging environment; testing on production is done by agreement and with care. Critical findings, such as personal data that anyone can download, are reported to you immediately rather than saved for the final report.
Fixes you can apply yourself
Every finding comes with a plain-language explanation, the evidence and a recommended fix. Because your app was built with AI, each finding also includes a ready-to-use fix prompt: paste it into the tool that built your app and it has the context it needs to solve the problem properly, instead of just hiding the symptom. Developers get the technical detail they need to review the change.
Once the fixes are in, we retest every finding and confirm it is closed. AI tools can quietly undo a fix when they regenerate code, so the retest is part of the service, not an optional extra.
You end up with a report you can show to customers, investors and partners: proof that your app was not only built fast, but also tested properly.
Who it is for
The test is designed for anyone who puts AI-generated code in front of real users:
- Founders and startups: before launch, before you start taking payments, and before an investor's due diligence
- Agencies and freelancers: before handing an app over to your client, as independent evidence of quality
- SMEs: when internal tools built outside the IT department start handling customer, employee or financial data
- Development teams: when AI assistants write more code than your reviews can realistically cover
We recommend testing again after major changes. With AI tools, a single prompt can rewrite large parts of an application, including code that was secure before.