Vibe Coding Security: Risks of AI-Generated Code and How to Ship Safely

Vibe coding is a software development practice where you describe the desired outcome in natural language and an AI tool writes the implementation. Tools such as Cursor, Claude Code, GitHub Copilot, and Lovable are commonly used for this approach. It shows up most often in early-stage products, internal tools, Minimum Viable Products (MVPs), and side projects that grow into businesses, where getting something in front of users matters more than getting the architecture right.

Security becomes a concern because vibe coding and security pull in opposite directions by default: the person shipping often cannot read the code closely enough to spot a flaw, and the model has no idea which parts of your app are security-critical.

AI coding tools learn from public repositories, and public code carries plenty of injection flaws, weak authentication, broken access control, and permissive defaults. The model reproduces those patterns at speed because they appear constantly in training data and they run without errors.

Benchmarks bear this out. In BaxBench, a 392-task backend benchmark published in 2025 by ETH Zurich covering 6 languages and 14 frameworks, the strongest model tested reached only 62% functional correctness, and researchers successfully exploited around half of the programs that did work correctly. Veracode’s 2026 report, which tested more than 100 models against 80 curated tasks, found that 45% of the generated code introduced a known OWASP vulnerability.

That second number counts code that already passed functional review. Working software and safe software get measured separately, and vibe coding tools only optimize for the first.

Secrets leak before anything else. A 2026 Cloud Security Alliance research note found that AI-assisted commits expose secrets at 3.2%, more than double the 1.5% rate of human-only commits, against a backdrop of 28.65 million hardcoded secrets discovered in public GitHub repositories during 2025.

Review capacity is the second gap. The same research note cites an analysis of 320 AI-authored pull requests that carried 2.74 times more application security issues per pull request than human-authored ones. Output grows faster than the review meant to catch it, and that mismatch sits behind every risk in the next section.

AI-generated code can introduce familiar security weaknesses much faster and at a much larger scale than traditional manual development.

One case makes the stakes concrete. In March 2025, the founder of EnrichLead announced he had built and launched a paid lead-generation service with zero hand-written code. Two days later, he posted that he was under attack, with API keys maxed out and users bypassing the subscription while junk records filled the database. As Snyk documented, the app had shipped with credentials exposed in the frontend and without authentication or rate limiting in place. He shut it down permanently within a week.

The risks below are known vulnerability classes, but AI-generated code can introduce them faster and at a larger scale than manual review was built for.

The map groups the failure modes we see most often when we audit AI-generated code.

Risk: AI reproduces injection flaws, weak authentication, broken access control, and permissive CORS settings from the public code it learned on, and it does so in the parts of your app that matter most. The pattern compiles and the feature works, so the flaw stays invisible until someone probes it.

Prevention: treat every AI output as untrusted and read the generated logic before it merges. In our own AI-assisted delivery, a senior engineer owns the review of anything touching authentication, payments, personal data, or third-party API credentials.

Risk: AI suggests open-source packages that may be outdated, unmaintained, malicious, or entirely invented. Attackers watch for those invented names and register them, so a hallucinated import turns into a live supply-chain vector.

Prevention: confirm that every suggested package exists under exactly the name you expect and is actively maintained before you install it, then let automated dependency scanning check versions on every build.

Risk: generated code embeds API keys, database credentials, access tokens, and connection strings directly in source files, and it picks permissive settings because permissive settings make the demo work. A public repository then does the rest.

Prevention: keep every credential in environment variables or a secrets manager, set repositories and storage buckets to private by default, and rotate any key that has ever been committed.

Risk: AI skips input sanitization and proper access control unless you ask for them, which leaves SQL injection, cross-site scripting (XSS), command injection, and unauthenticated endpoints wide open. A hand-rolled login generated in a single prompt is the most common version of this.

Prevention: validate and sanitize every user input, use parameterized queries for database access, and hand authentication to an established provider such as Auth0 or Firebase instead of writing it yourself.

Risk: teams accept code they did not write and later cannot explain what changed or which dependency arrived alongside it. When something breaks in production, nobody can reconstruct the decision that caused it.

Prevention: keep a human review gate on every AI-generated change and a record of what the model produced, so the codebase stays explainable months later.

The same review discipline applies when AI touches an existing codebase rather than a new one. We covered where it helps and where it falls short in our guide to AI for application modernization.

Securing AI-generated code means moving from occasional review to security that runs alongside creation. Treat vibe code security as a set of layers rather than a single gate before launch, because the failures come from different places and each one has its own fix. At Cleveroad, we begin with a code audit that maps dependencies, finds exposed secrets, checks access control, and sets a security baseline before anything ships.

The table below pairs each vibe coded app security layer with the action that closes it and the reason that action matters.

Two rows carry more weight than the rest. Secrets and dependencies are where AI-generated code fails most often, and both cost very little to fix before launch and a great deal after a leak.

Order matters as well. Running dependency scanning before you have moved secrets out of the codebase gives you a clean report on a repository that still leaks credentials, so work down the table from the top rather than picking the row that looks easiest.

The prompt shapes the safety of the output, so name the security property you want. Generic instructions produce generic code, and generic code defaults to whatever pattern was most common in the training data.

These are the vibe coding security best practices worth keeping in your prompt rotation:

• "Connect to the database using environment variables for credentials, and fail loudly if a variable is missing."
• "Add input validation and use parameterized queries for every database call in this file."
• "Review this code for injection, broken access control, and missing authorization checks, and list what you find before changing anything."
• "Add authentication using Auth0 rather than a custom login, and restrict these endpoints to authenticated users only."

Asking the model to list findings before it edits is the highest-value habit in that set. It separates diagnosis from repair and leaves you something concrete to check.

Static analysis (SAST) and software composition analysis (SCA) belong in CI/CD rather than in a quarterly review, so every change gets checked automatically. Dependency alerts go in the same place.

Start in report-only mode so the team sees findings without blocked merges, then switch the high-severity rules to blocking once the backlog clears. Securing vibe coding output this way is standard on our client work, and it is part of our DevOps and CI/CD services. Pairing the scanners with automated end-to-end tests also means a security fix cannot quietly break a working feature.

Once your app handles live customer data or real payments, an outside audit stops being optional. The trigger is exposure rather than codebase size: a 2,000-line app processing card payments needs a review more urgently than a 50,000-line internal tool.

An audit is also the cheapest way to find out what you actually have after months of AI-generated commits. We walk through the process end to end in our guide on how to conduct a code audit successfully, including the reports to expect and the questions worth asking your auditor.

Cleveroad is an IT vendor with profound experience in software delivery, including AI-powered software, GenAI, IDP solutions, etc. We work on vibe coding security from two directions. The first is rescuing and auditing apps that are already built, where an independent code audit maps what the AI produced and what it left exposed. The second is AI-assisted development services, where our engineers use AI tools on client code with a human reviewing every generated change before merge, under ISO 9001 and ISO 27001 practices.

Our client, Proprio Cloud Solutions, gave us the clearest test of the AI-assisted development model. The Michigan-based SaaS company runs its Orion platform on NetSuite-integrated procurement and quoting workflows for the contract furniture industry, so client data and access control were never something we could trade for delivery speed. Proprio Cloud Solutions reached out to us to modernize its legacy contract furniture workflows and improve Orion’s procurement, quoting, and operational capabilities within NetSuite.

The screen below shows the Field Service mobile app MVP we delivered for Proprio alongside the platform releases, built under the same review gate.

Our four-person team used Claude Code across engineering and QA, with a senior engineer reviewing every generated change before merging. ISO 27001-aligned review and access policies stayed in place throughout, and we replaced a full day of manual regression testing per environment with automated Playwright coverage running in CI/CD across three client environments.

As a result, sprint output rose 30% to 40% at the same team size while quality held: code-review pass rate stayed steady, and bug counts stayed flat. Test script creation dropped from a full day to roughly two hours. Our team also delivered four major Orion releases on schedule and built a Field Service mobile app MVP with work order management and offline crew tracking.

• The full AI-Assisted ERP Development for NetSuite SaaS Platform case study covers the engagement in detail.

Cleveroad brings the expertise needed to turn AI-generated code into a more secure, production-ready application, with benefits including:

• Independent code audits that report what is actually in your repository, with no incentive to defend the code
• A human review gate on every AI-generated change, so nothing merges unread
• ISO 9001 certified quality management and ISO 27001 certified information security management
• Senior engineers backed delivery history across FinTech, Healthcare, Logistics, and Retail since 2011
• A 280-engineer in-house team plus a 2,100-specialist external talent network, so a review can scale into remediation without a new vendor search