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AI can generate code. Cloud platforms can deploy it. But when attackers target it, only real security engineering can protect it.
Vibe coding has changed how applications get built. Open an AI tool, describe the idea, generate the frontend, create the backend, connect the database, push the code, deploy it on the cloud, and within hours something that looks like a finished product is live.
The login page opens. The dashboard loads. The API responds. The deployment succeeds.
But there is a question that matters more than any of that: is the application working, or is it actually secure?
Production is not just features. Production means real users, real data, real payment information, real cloud bills, and real attackers actively looking for a way in.
The risk with vibe coding is not that AI writes code. The risk is deploying AI-generated code without understanding the security gaps hidden inside it. An attacker does not care how the code was written or how fast it shipped. An attacker only cares about one thing: where the application can break.
AI can create forms, APIs, database queries, authentication flows, admin dashboards, Docker files, and cloud deployment scripts faster than a team could write them manually. That speed is genuinely useful.
But AI-generated code does not automatically include safe input validation, secure authentication, proper authorization, injection protection, secret key protection, rate limiting, or secure deployment configuration. These are judgment calls, not defaults.
A demo only needs to work. A production system needs to survive abuse. That difference separates a vibe coder from a production-grade developer, and it is the gap this checklist is built to close.
Server-Side Template Injection (SSTI) happens when user-controlled input is passed into a template engine in a way that lets the engine treat it as executable logic instead of plain text. A welcome message, invoice, report, or notification built from dynamic values can become an entry point if the template engine is not handling that input safely.
When this goes wrong, an attacker may read sensitive server files, access environment variables, extract secret keys, or execute commands on the server.
AI-generated template logic often works correctly during normal testing, because normal users enter normal values. Attackers do not. They enter payloads designed to break the boundary between data and code.
Fix it: never execute user input inside templates, escape user-controlled values properly, use safe rendering methods, enable sandboxing where available, keep debug mode disabled in production, and avoid exposing detailed template errors to users.
Regular Expression Denial of Service (ReDoS) happens when a poorly constructed regex takes an excessive amount of time to process a specially crafted input. AI tools readily generate long, complex validation patterns for emails, passwords, URLs, and phone numbers. They work for normal inputs, so they get deployed.
An attacker then sends a crafted string that causes the regex engine to backtrack excessively. CPU usage spikes, requests time out, and a single input field becomes a denial-of-service weapon.
Fix it: avoid overly complex regex patterns, use trusted validation libraries instead of custom patterns, set maximum input length before validation runs, add execution timeouts where supported, and test validation logic against long and malicious inputs, not just valid ones.
Most developers set a minimum password length and forget to set a maximum. That gap is dangerous, because password hashing algorithms are intentionally slow by design. If an attacker submits a password tens of thousands of characters long, the server still tries to hash it, and that single request can consume disproportionate CPU time.
The attacker does not need valid credentials. They only need to make the server work harder than it should.
Fix it: set a reasonable maximum password length, limit request body size at the server level, add login rate limiting by account and by IP, and monitor for abnormal spikes in authentication traffic.
Hardcoding secrets directly into source code is one of the most common vibe coding mistakes. AWS keys, database passwords, JWT secrets, SMTP credentials, and API tokens often start as placeholders that get replaced with real values during testing, with the intention of moving them later. That step gets skipped, and the code reaches a repository with live credentials inside it.
Once a key is exposed, automated scanners can find it within minutes. A leaked credential can lead to cloud bill abuse, database access, or full infrastructure compromise.
Fix it: never hardcode secrets, use environment variables and a secret manager, add secret scanning before every commit and inside CI/CD, rotate any leaked key immediately, apply least-privilege cloud permissions, and use separate keys for development, staging, and production.
Injection happens when user input is used directly inside a database query without proper handling, whether the backend is SQL-based or running on MongoDB or another NoSQL store. AI-generated demo code is often optimized to make the login work, using quick queries or direct input mapping that holds up for correct credentials but fails under malicious input.
An attacker who finds this gap can bypass login, extract data, modify records, or take over accounts entirely.
Fix it: use parameterized queries everywhere, avoid raw string concatenation in SQL, validate input schema strictly for NoSQL, never accept raw query objects from users, apply least-privilege database permissions, and review every login, search, and admin query specifically for this risk.
Vibe coding involves constant copy-pasting of commands from documentation, AI tools, forums, and tutorials. Clipboard attacks exploit the fact that text visible on a page is not always the text that actually gets copied. A command that looks like a safe installation step can paste a different, malicious command into the terminal.
The result can be malware execution, token theft, SSH key compromise, or a backdoor installed on a developer machine, with no indication anything went wrong until later.
Fix it: paste commands into a plain text editor before running them, read every command before execution, avoid unknown curl or wget pipe-to-shell scripts, verify package names against official sources, and protect SSH keys with passphrases.
A replay attack happens when an attacker captures a valid request and resends it. If the application does not check freshness through a timestamp, nonce, or signature, the same request can be processed more than once. This matters most for OTP verification, password reset links, payment callbacks, and webhooks.
AI-generated APIs are usually built to handle a request correctly the first time. Replay protection is rarely part of that default, because the request works once and the developer assumes that is sufficient.
Fix it: use HTTPS everywhere, add timestamps and nonces to sensitive requests, verify webhook signatures, make OTPs short-lived and strictly one-time use, use short-lived tokens, and detect duplicate transaction IDs before processing them.
Authentication answers who a user is. Authorization answers what that user is allowed to access. AI tools generate working CRUD operations quickly, but the create-read-update-delete logic frequently checks whether a user is logged in without checking whether that user owns the specific record being requested.
A predictable record ID in a URL is enough to expose this gap. Changing an invoice or order number in the address bar should not reveal another customer's data, but in many AI-generated applications, it does.
Fix it: check ownership on every request, add role-based and object-level access control, protect admin APIs separately from user-facing ones, avoid exposing predictable sequential IDs, and test specifically for horizontal and vertical privilege escalation before launch.
Cross-Origin Resource Sharing (CORS) errors are common during development, and the fastest way to make them disappear is to allow requests from any origin. AI tools frequently suggest this as a quick resolution, and it gets left in place after the error stops appearing.
An overly permissive CORS policy, especially one combined with credentials enabled, allows any website on the internet to make authenticated requests to the application on a logged-in user's behalf. This turns a development convenience into a direct path for cross-site data theft and account compromise.
Fix it: never use a wildcard origin alongside credentials, maintain an explicit allowlist of trusted origins, separate CORS rules for development and production environments, and review CORS configuration as a required step before any production deployment, not an afterthought once errors stop.
File upload features for profile pictures, documents, or attachments are common in AI-generated applications, and they are also one of the easiest paths to a server compromise when built without restrictions. Accepting any file type, any file size, and storing files without renaming or isolating them gives an attacker room to upload executable scripts disguised as images or documents.
If an uploaded file ever gets executed by the server, or if oversized uploads are allowed without limits, the result can range from storage exhaustion to full remote code execution.
Fix it: restrict uploads to an explicit allowlist of file types, validate file content and not just the file extension, enforce maximum file size limits, store uploaded files outside the web root or in isolated storage such as cloud object storage, rename files on upload, and disable script execution in upload directories.
AI is a genuinely useful tool for moving faster, generating boilerplate, building prototypes, and accelerating deployment. It is not a substitute for secure engineering judgment.
A developer working with AI-generated code still needs to understand how input is handled, how authentication and authorization are enforced, where secrets live, how APIs can be abused, and how the system behaves when something goes wrong. AI can generate the code. It cannot take responsibility for what happens when that code reaches production.
If the answer is uncertain for several of these points, the application is not production-ready. It may be working. It is not yet secure.
Vibe coding itself is not the problem. Deploying without a security review is. AI helps build faster, but attackers move just as fast against anything left exposed.
The real test of an application is not whether it runs. It is whether it survives when someone deliberately tries to break it.
