AI Code Audit

An AI code audit is an independent review of a codebase — AI-generated or hand-written — that checks it for security vulnerabilities, architectural weaknesses, and production-readiness gaps before real users find them. It ends with a severity-ranked findings report and a clear path to fix what it finds.

Most of what we audit today started in Cursor, Bolt, Lovable, Claude, or ChatGPT — fast to prototype, rarely production-ready on day one. But the audit itself is not limited to AI output: the same categories of risk, and the same process, apply to a codebase nobody used AI to write. Whether you're a team shipping fast with AI tools and want a check before the next release, or a founder who inherited an AI-built app and needs to know what's actually in it, you get the same audit.

What We Audit

Seven categories, every time — not just the security scan most "AI code review" tools stop at.

What You Get

An audit is not a PDF that sits in a drawer. Three things come out of it.

Two Ways to Start

People come to us for an audit for one of two reasons. The findings report looks the same either way — what differs is where we start looking.

Auditing an AI-Generated or Vibe-Coded App

You (or a non-technical founder, or a contractor) used Cursor, Bolt, Lovable, Claude, or ChatGPT to build something that works — it demos well, early users like it, and now it needs to survive contact with the real world. We start where these tools consistently fail: hardcoded secrets, authentication that checks the box but not the request, and API usage that was never built to a budget. Our playbook for this exact handoff is in Shipping an AI-Built Prototype to Production.

AI-Accelerated Audit of Any Codebase

Your codebase does not have to be AI-generated for this to apply. We run the same automated analysis — static analysis, dependency scanning, secrets detection — against any stack, AI-assisted or not, which is what lets us turn an audit around in weeks instead of months. The manual review on top is where our engineers actually read your architecture and business logic; the automation just means less of the budget goes to finding the obvious stuff by hand.

What Goes Wrong in AI-Generated Code

We've audited dozens of AI-generated codebases. The same categories of problems appear in nearly every one — systematic blind spots, not edge cases.

These numbers come from independent research: Veracode tested 100+ LLMs and found 45% of generated code fails security tests. Apiiro's 2025 analysis showed AI-assisted developers expose credentials nearly twice as often. NYU researchers found ~40% of Copilot-generated programs contained vulnerabilities, while Stanford's study confirmed developers using AI assistants produce less secure code — and are more confident it's safe. Our own review of agent-written Flutter is in How AI Agents Struggle with Flutter: duplicated state, no tests, and UI that never got a second look from a human.

Security Vulnerabilities

1. Exposed API Keys and Secrets

AI tools frequently hardcode API keys, database credentials, and third-party service tokens directly in source code. These end up in public repositories, client-side bundles, or environment files that ship to production. One leaked OpenAI key can generate thousands of dollars in unauthorized usage within hours.

2. Missing Authentication and Authorization

AI-generated apps often implement authentication at the surface level — a login screen exists, but the backend doesn't actually verify permissions. API endpoints accept any request. Admin routes are accessible without role checks. User A can see User B's data by changing an ID in the URL.

3. Injection Attacks

SQL injection, XSS, and prompt injection are pervasive in AI-built code. AI models generate code that concatenates user input directly into queries, HTML, or LLM prompts without sanitization. A single vulnerable endpoint can compromise your entire database or allow attackers to manipulate your AI's behavior.

4. Insecure Data Storage

Personal data stored in plain text, session tokens in localStorage, passwords hashed with MD5 or not hashed at all. AI tools default to the simplest implementation, which is rarely the secure one. GDPR and privacy compliance is typically absent. Our guide to encryption in production applications covers how data should actually be protected at rest and in transit.

API Cost Optimization

1. Redundant API Calls

The most expensive problem we find. AI-generated apps make unnecessary calls to paid third-party APIs — geocoding services, financial data providers, AI models, verification APIs. They call the same endpoint multiple times for the same data, fail to cache responses, and make requests that could be avoided entirely with simple local logic. We routinely find apps where 60-80% of API spend is wasted.

2. Missing Rate Limiting and Budgets

No per-user rate limits. No daily spend caps. No circuit breakers when API costs spike. A single user — or a bot — can burn through your entire monthly budget in a day. AI tools never implement cost controls because they have no concept of your business model or the pricing tiers of the services you integrate.

3. No Caching or Batching

Fetching the same exchange rate, IP geolocation, or user profile from a paid API on every single request instead of caching it. Making individual API calls in a loop instead of using batch endpoints. Each unnecessary request costs money, and at scale these add up to thousands of dollars per month.

Data Leaks and Privacy

1. Logging Sensitive Information

AI-generated code loves verbose logging. User emails, passwords, payment details, and personal data end up in application logs, error tracking services, and third-party analytics. These logs are often accessible without authentication and retained indefinitely.

2. Oversharing Through APIs

API responses that return entire user objects — including hashed passwords, internal IDs, email addresses, and metadata — when the frontend only needs a display name. GraphQL endpoints without depth limits that allow attackers to extract your entire data model.

3. Third-Party Data Exposure

AI tools integrate analytics, error tracking, and monitoring services without considering what data flows to them. User behavior, personal information, and business data end up in third-party systems without consent, violating GDPR and other privacy regulations.

Who's Behind the Audit

Nerdy Production is led by Ilya Nixan, previously CTO of QIWI, one of the largest payment platforms in its market, where he ran roughly 12 engineering teams covering everything from web products down to card processing and PCI-DSS scope. That is the standard our audits are held to: not "does the code run", but "would this survive a review by someone who has carried compliance scope and had to answer for a production incident." We do not hold PCI-DSS or SOC 2 certification ourselves, and we will tell you plainly when a finding is outside what an audit can certify — but the person setting the bar for what counts as a critical finding has operated regulated infrastructure, not just read about it.

Our engineers work with AI coding tools daily, which is exactly why we know where they fail. How AI Agents Struggle with Flutter walks through the specific gaps an unsupervised agent leaves behind — duplicated state, missing tests, UI that never got a second look — the same patterns we look for in every audit, regardless of language or framework.

How an Audit Works

We deliver a complete audit with actionable fixes — not just a list of problems.

1. Codebase Access and Scope

You grant us read access to your repository and deployed environment. We define the audit scope based on your priorities: full audit or focused on specific areas (security, costs, or data privacy). No changes are made to your code during the audit phase.

2. Automated Analysis

We run static analysis, dependency vulnerability scans, secrets detection, and API cost profiling against your codebase. This catches the low-hanging fruit — known vulnerabilities, exposed credentials, outdated packages with security patches, and obvious performance issues.

3. Manual Expert Review

Our engineers manually review the architecture, business logic, authentication flows, API integrations, and data handling. This is where we find the problems that automated tools miss: logic flaws, authorization gaps, cost optimization opportunities, and design issues that will cause problems at scale.

4. Severity-Ranked Report

We deliver a detailed report with every finding categorized by severity (critical, high, medium, low) and type (security, cost, privacy, performance). Each finding includes a clear explanation of the risk, proof of concept where applicable, and a specific fix recommendation with code examples.

5. Remediation — Your Choice

You pick who fixes it. We can implement every fix ourselves as a fixed-scope engagement, with critical security patches first and you reviewing every change before it's merged. Or, if you'd rather your own team own the fixes, we can embed an engineer who already knows your findings report through team augmentation instead of handing you a document and disappearing. Either way, the result is a codebase you can deploy with confidence.

AI-Generated Code vs Production-Ready Code

What changes when your codebase goes through a professional audit

AI Code Audit Pricing

Transparent pricing based on your codebase size and audit scope

Frequently Asked Questions

Common questions about auditing AI-generated and production codebases