Vibe Coding Security Risks: What a Software Audit Finds Inside AI-Built MVPs

Creating an app with minimal effort using AI seems like a dream come true until you understand the exact scope of vibe coding security risks. Many businesses have already learned this through extremely painful experiences, such as Moltbook, which lost 1.5 million API authentication tokens, 35000 email addresses, and private messages within three days of its launch.

The truth of the matter is that the Moltbook situation isn’t some edge case. It’s what happens when you ship a vibe-coded MVP without an audit. The pattern we see across AI-built MVPs is consistent, and it’s not subtle. In this post, we rank the seven vibe coding security risks that show up most often in audits, explain why they compound silently, and give you a stage-based checklist you can run before soft launch, before your first paid pilot, and before a seed round.

In addition, we explain how professional vibe code cleanup covers the top-risk surfaces: code, configuration, auth, and API to help ensure that every part of the product is secure through every iteration, all the way to production.

Why Vibe Coding Risks Aren't Just "Junior Developer" Bugs

It’s perfectly normal for junior developers to make mistakes. However, during regular, read, non-vibe-coded software development, those mistakes usually get caught before any damage is done.

Usually, a software development company has several safety nets in place to review a junior developer’s work before it reaches real users. There are automated tools that scan the code for common mistakes, and a senior developer to review every change. Security tools flag any leaked passwords or login keys, while someone on the team spends time thinking carefully about how an attacker might try to break things. Between them, these layers catch most of the obvious problems before anything goes live.

Vibe-coded apps usually lack these additional review steps, which leads to three major issues:

• The Same Mistakes Keep Coming Back
  Every time you ask the AI to add a new feature, it can accidentally undo a security fix you made the day before. The AI has no memory of what it corrected yesterday, so problems you thought were solved quietly return.
• Nobody Really Owns the Code
  If no human actually wrote a line of it, no human can fix it when things go wrong. When something breaks at three in the morning, and users are locked out, ‘ask the AI to try again’ is not a real plan.
• Good Demo ≠ Safe with Real Users
  The AI is rewarded for producing code that runs and looks correct. It is not rewarded for producing code that holds up when someone actively tries to break in.

All of these claims are supported by real data, including Veracode, which tested more than 100 AI coding tools and found that AI-generated Java code failed basic security tests 72% of the time. In 86% of cases, the AI wrote code that would allow attackers to run harmful scripts through a website. A separate study by Apiiro that examined Fortune 50 companies found that AI-assisted code contained 322% more ways for attackers to break in and take control and 153% more deep structural flaws than code written by humans. Between December 2024 and June 2025, AI-generated code was linked to roughly 10,000 new security problems every month. That is ten times more than just six months earlier, and this pile of security problems is growing bigger every month.

Top 7 Vibe Coding Security Risks, Ranked by Audit Frequency

The risks of vibe coding we see most often while doing code cleanup are recurring. It means they are the same, no matter what type of app you create or what tool you use to do it. We’ve ranked the list below based on what auditors actually see in real-life cases, not by what’s easiest to scan for.

Hard-Coded Secrets in Client-Side Code

This is the most common vibe coding security risk we’ve seen. API keys, database URLs, service credentials, and authentication tokens are bundled into JavaScript files that ship directly to the browser. Then, you just need to open DevTools, view source, and there they are.

The Moltbook breach, documented by Wiz Research, is the textbook example of this issue. A Supabase API key was stored in a client-side bundle with Supabase’s Row-Level Security (RLS) disabled, granting anyone with a browser full access to the production database. There was no exploit required, no sophistication needed, just curiosity, and you get access to data.

What an audit catches: secret scanning across the repo and bundled assets, plus a config review of every backend service the app talks to.

Broken Authentication and Authorization

AI tools generate login flows that look right in a demo, but they often fail silently against real attackers.

Common patterns we find:

• Admin routes are protected only by a client-side flag, such as isAdmin. Therefore, a user flips it in the browser and gets in.
• Endpoints that check if you’re logged in, but not whether you should see this user’s data (Broken Object-Level Authorization, or BOLA).
• Password reset flows that email a token without verifying the requesting identity.
• Session cookies shipped without HttpOnly, Secure, or proper expiration.

For example, consider that in July 2025, Wiz Research found this exact class of flaw in Base44, a popular vibe-coding platform. A publicly visible app_id was enough to create a verified account inside someone else’s private app.

What an audit catches: server-side auth checks on every route, BOLA and IDOR testing across each resource endpoint, and session hardening.

Misconfigured Backend-as-a-Service (BaaS) Rules

Supabase, Firebase, and similar platforms make it easy to ship vibe-coded apps, but they also default to permissive setups that are only safe if you know how to lock them down. For example, the Tea app breach in 2025 exposed 72,000 user images, including 13,000 government-issued IDs and selfies, because a Firebase bucket was left open without authentication. Plenty of smaller apps we’ve reviewed ship with the database rule set to “allow read, write: if true.”

What an audit catches: review of every BaaS rule, RLS policy, and storage bucket ACL, plus tests that try to read and write data as an anonymous user.

Injection Vulnerabilities (SQL, NoSQL, Command)

LLMs love string interpolation, but string interpolation loves to get injected. So, when a model writes a database query, it often does the natural-language thing and drops the user variable straight into the SQL. It works in testing with benign inputs, but breaks the moment a real user types an apostrophe or drops a table.

The OWASP LLM05:2025 guidance on improper output handling flags this exact pattern: an AI-generated database layer that builds queries from user prompts can be tricked into destroying the data it was meant to query.

What an audit catches: manual inspection of every data access layer, enforcement of parameterized queries, and fuzz testing of user-facing inputs.

Missing Input Validation and Output Sanitization

Generated code trusts user input, but that trust should not extend to real users. Therefore, without validation on every field and sanitization before any data hits a database or is rendered in HTML, you’re one crafted payload away from a stored XSS, HTML injection, or worse.

Veracode’s study found that AI tools failed to defend against cross-site scripting (CWE-80) in 86% of relevant samples. At that point, it’s a default vibe coding security risk rather than a rounding error.

What an audit catches: schema validation on every API boundary, output encoding on every render path, CSP headers, and DAST testing of the running app.

Insecure and Hallucinated Dependencies

Here’s a risk most founders haven’t heard of: slopsquatting. It comes from LLMs, which sometimes invent package names. They suggest importing fastjson-pro even though no such library exists on npm or PyPI. Attackers watch for these hallucinations, register the fake packages, and wait for the next developer to copy-paste the suggestion.

Even when the packages are real, they can be compromised. For example, in August 2025, the popular Nx build system was hit by a supply chain attack where attackers merged a malicious pull request and then used Claude, Gemini, and Amazon Q command-line tools on infected machines to scan for crypto wallet credentials. Users had their wallets drained before anyone noticed.

What an audit catches: Software Bill of Materials (SBOM) generation, dependency scanning, and verification that every imported package actually exists on its official registry.

Exposed Infrastructure and Overly Permissive Defaults

The last common vibe coding risk is quite boring, but it’s still a factor that compromises hundreds of systems. Public S3 buckets, wide-open CORS, admin panels on the open internet with no IP allowlist, debug endpoints live in production, no rate limiting on authentication routes, and logs that happily record passwords and tokens are all part of that risk. However, instead of being coding flaws, they are deployment flaws.

AI tools generate infrastructure-as-code the same way they generate application code: optimized for ‘it runs’ and indifferent to ‘it’s locked down’.

What an audit catches: infrastructure review, CORS audit, secrets-in-logs check, rate limit verification, and network-level access control.

Why These Vibe Coding Risks Compound Instead of Staying Small

The cleanup is always harder than people expect because each vibe-coded iteration optimizes for ‘still works’. Therefore, it doesn’t optimize for ‘still safe’, so a patched bug can come back the next time you prompt a related feature. For example, a tightened permission can loosen again when you ask for ‘easier onboarding’ and the code drifts toward its demo-friendly default.

Static analysis tools help, but they miss more than they catch. Apiiro’s research found that AI-assisted code raises precisely the class of issues scanners miss, such as broken auth flows, insecure designs, and systemic architectural weaknesses. These are deep issues that reviewers struggle to spot, and scanners weren’t built to find. Therefore, the real work that matters is still human.

The Minimum Viable Vibe Coding Audit, by Stage

It’s essential to understand that you don’t need a full enterprise audit on day one. Instead, you will be best served by choosing the specific type of audit that fits your development stage. This way, you can get the best value for your money and prevent going over budget.

Before Soft Launch (Real Users Are About to See This):

• Secret scan across the full repo and bundled client assets
• Checking for every BaaS rule review, RLS, Firebase rules, and storage buckets (must not be public)
• Every endpoint is authenticated on the server, and there are no client-side flags in charge of who sees what
• Dependency scan to remove or replace anything unverified
• CORS configured to your actual domains

Before a Paid Pilot (Money Is About to Change Hands):

• Authentication and session logic were pentested properly
• BOLA and IDOR checks across every resource endpoint
• Input validation on every form field and API parameter
• Logs reviewed for PII and secrets leakage
• Rate limiting is active on authentication, payment, and write-heavy routes
• Backup and restore have been tested at least once

Before a Seed Round (Due Diligence Is Coming):

• Third-party code review with a written report that investors can see
• Threat model documented and shared with the engineering lead
• Software Bill of Materials generated and kept current
• Penetration test report, no older than 90 days
• Incident response runbook and a secrets-rotation procedure, both written down
• Data handling is mapped to whichever framework matters for your market (GDPR, HIPAA, SOC 2 prep)

What a Professional Vibe Coding Audit Actually Covers

The dark and dangerous spiral that vibe coding leads to is that people are tempted to leave everything to the machine, including code review. However, the reality is that even when an LLM runs an audit and gives you an extensive list of issues, much of it will be wrong, and the most dangerous issues won’t be on it.

It happens because automated scanners, including AI-based ones, don’t know your threat model. They don’t know which tables hold PII, and they don’t know that the /admin/refund endpoint is the one that moves money. They also don’t know your compliance scope, report on what matches a pattern, and miss what actually matters.

A structured vibe coding audit covers the things scanners skip:

• Source code review by engineers who’ve seen these failure modes before
• Architecture review focused on trust boundaries, data flows, and blast radius
• Configuration review of every BaaS, cloud, and deployment setting
• API testing with an adversarial mindset, not a checklist
• Dependency audit including hallucinated, abandoned, and typosquatted packages
• Threat modeling against your actual business, your actual users, your actual attackers

This is exactly what Redwerk’s vibe code cleanup and software audit services deliver. We don’t have junior engineers reading generic AI-generated scan reports and ‘fixing’ whatever catches their eye. Instead, our senior team has years of experience in turning messy codebases into products that hold up under real traffic. We even helped audit and future-proof a fintech fraud detection platform for Project Science, increasing backend API maintainability by 80%, so vibe-coded apps are familiar territory.

Bottom Line: Safe Way to Manage Vibe Coding Risks

Vibe coding isn’t going away, and it really shouldn’t, because this technology offers great opportunities when used well. Using AI-assisted development, in any form, often puts a product in front of users in days rather than months.

However, the part that needs to change is what happens between ‘it works in the demo’ and its actual release to users. That gap is where the 45% of OWASP-vulnerable code lives and where vibe coding risks hiding in your codebase stop being theoretical and start costing you customers.

You don’t need to slow down shipping to launch a comprehensive vibe code review to identify weaknesses and patch them, increasing overall product security and reliability. And to do this, you will need the right set of eyes on the code and experienced reviewers who can develop an effective security strategy.

If you’ve vibe-coded your MVP and you’re close to launch, a paid pilot, or a funding round, talk to us. We’ll tell you straight what’s broken, what’s salvageable, and what to fix first.

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