AI-Generated Code Audit

The Problem with AI-Generated Code

AI coding tools like Cursor, Bolt, Lovable, and ChatGPT make it possible for anyone to build a working application. The prototype looks great. It runs locally. You show it to investors or early users and get positive feedback. Then you deploy to production — and the problems begin.

AI agents write code that works, but they don't write code that's production-ready. They don't think about what happens at scale. They don't consider what an attacker would do. They don't optimize for cost. The result is an application that functions correctly in demos but bleeds money, leaks data, or breaks under real traffic.

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.

What Goes Wrong in AI-Built Applications

We've audited dozens of AI-generated codebases. The same categories of problems appear in nearly every one. These aren't edge cases — they're systematic blind spots that AI coding tools consistently miss.

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.

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.

Our Audit Process

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

How It Works

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. Report and Prioritization

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. Fix Implementation

Optionally, we implement the fixes ourselves. Critical security patches are applied first, followed by cost optimizations and privacy improvements. You review every change before it's merged. The result is a production-ready codebase you can deploy with confidence.

AI-Generated Code vs Production-Ready Code

What changes when your codebase goes through a professional audit

AI-Generated Code Audit Pricing

Transparent pricing based on your codebase size and audit scope

Frequently Asked Questions

Common questions about auditing AI-generated code