The Vibe-Coded App Security Audit: What We Check First
You described the app, the AI built it, and it works. That part is real. The part nobody shows in the demo video is what happens when the app meets its first hostile visitor, and every public app eventually meets one. An AI code security audit answers one question before that day arrives: what did the tool leave exposed?
We run this audit as the first phase of every production review, and the findings repeat. AI-generated code tends to fail in the same handful of places, app after app, regardless of which tool wrote it. That makes the audit fast, and it makes the first four checks predictable. Here is what they are, why each one matters, and what fixing them involves.
Why AI-Written Code Fails in Predictable Ways
A coding assistant optimizes for one outcome: code that runs and does what you asked. You asked for a working app, so that is what it built. You did not ask it to assume someone would feed the app malicious input, probe its endpoints directly, or read its source code looking for keys, so in most cases it didn’t.
Security is largely the practice of handling the requests nobody legitimate would ever send. That work is invisible in a demo. It adds no features, and the app behaves identically with or without it, right up until someone tests the difference.
The useful consequence is that vibe-coded app security problems cluster. The tools share training data, and they share the habit of building the happy path first. So instead of auditing blind, we start with the four exposures that turn up most often in AI-built apps. They are well documented across the industry, and they map to the oldest categories of web vulnerability, now reproduced at generation speed.
Check One: Credentials and API Keys Committed in the Code
The first thing we search for is secrets sitting in the source code: API keys, database passwords, payment tokens. AI assistants paste credentials directly into code because that is the shortest path to a working result. The tool needed the key to make the feature work, so the key went where the feature was.
This matters more than it sounds like it should. If your code lives in a repository, everyone with access to that repository has your keys. If the repository was ever public, even briefly, automated scanners may have already harvested them; bots watch public code hosting for exactly this, and they often find exposed keys within minutes of exposure. A harvested payment or model-provider key becomes someone else’s spending on your invoice.
The check itself is mechanical: scan the codebase and its full history for key patterns and credential formats. The history matters. A key removed in the latest version is still visible in every earlier commit, so “we deleted it” often means “it is one click further away.”
Check Two: Authorization That Exists Only in the Interface
The second check is whether permissions are enforced anywhere besides the screen. AI tools are reliable about authentication, the login step. They are much less reliable about authorization, which decides what a logged-in person is allowed to do. A common pattern in AI-built apps: the admin button is hidden from regular users, and that hidden button is the entire permission system.
Hiding a button is not a security control. The browser is software your visitor controls completely. Anyone can see what requests the interface sends and send their own version, including requests for screens and actions the interface never offered them.
We test this by ignoring the interface. We send requests directly to the application as a logged-in low-privilege user and watch what comes back. Can a regular account call the admin endpoints? Can user A fetch user B’s records by changing an ID number in the request? When the answer is yes, every piece of data in the system is effectively shared with every account, and in many cases with anyone who can reach the server at all.
Check Three: Injection Paths Through Unvalidated Inputs
The third check looks at what happens to the text people type into the app. Every form field, search box, and URL parameter is input, and somewhere downstream that input gets used: assembled into a database query, rendered onto a page, passed to another service. Injection is what happens when input gets treated as instructions instead of data.
The classic case is SQL injection, where a crafted entry in a form rewrites the database query around it. One well-formed string in a login box can read out the user table. The same shape of problem shows up as cross-site scripting, where submitted text runs as code in another visitor’s browser. These attacks are decades old, thoroughly documented, and still routinely effective, because the defense has to be applied at every input, and AI tools apply it inconsistently. Some generated code validates carefully. Some trusts whatever arrives. The same codebase usually contains both, which is why spot-checking one form tells you little.
The audit traces each input to where it is used and confirms it is validated and escaped on the way. Modern frameworks make the fix straightforward in most cases; the work is in finding every path, not in closing each one.
Check Four: Endpoints That Trust Whatever the Browser Sends
The fourth check is close kin to the authorization problem but broader: does the server verify anything, or does it assume the interface already did? AI tools often put validation in the browser, where it improves the experience, and then skip it on the server, where it would provide the actual protection.
The visible symptom is an app that politely blocks bad input on screen while the endpoint behind it accepts anything. The expensive version involves values the browser should never be trusted to set. A classic example is checkout code that accepts the price as part of the incoming order request. The interface always sends the right price, so everything looks fine. Anyone skipping the interface could name their own.
The test is the same discipline as check two: talk to the server directly and see what it accepts. Quantities, prices, account identifiers, role fields, anything consequential that arrives from outside gets verified again on the server, against data the server controls.
What Closing Each Exposure Involves, and What It Reduces
None of these findings means the app is a loss. Each has a known, bounded fix, and the fixes usually land in days, not months.
Exposed credentials move out of the code into environment variables or a secrets manager, and every key that was ever committed gets revoked and reissued. That reduces the blast radius of a leaked repository to roughly zero. Authorization moves to the server: every endpoint checks who is asking and what they own before it answers, which is what actually separates one customer’s data from another’s. Input validation gets applied at each path the audit traced, shrinking the injection surface. And server-side verification of consequential values closes the gap between what the interface suggests and what the system will accept.
To be clear about what this buys: a security pass reduces risk, it does not abolish it. No audit makes an application attack-proof, and anyone promising that is selling the wrong thing. What the pass does is close the specific exposures that AI-generated code leaves open most often, which are also the ones opportunistic attackers try first. For a newly launched app, that covers a meaningful share of the realistic threat.
The security pass is the first phase of every Production Readiness Assessment we run, and the findings arrive in writing: what we checked, what we found, and what each fix involves. Ask for one before launch, not after an incident. The first version of this conversation is a planning meeting. The second version is a cleanup.