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AI App Security Audit for AI-Built Apps

A focused security review for apps built with AI tools such as Lovable, Bolt, v0, Replit, Cursor and Claude Code. We find exposed keys, open database rules, broken authentication and AI-feature risks like prompt injection — and give you a prioritised, plain-language fix plan.

Overview

What is an AI app security audit?

An AI app security audit is a structured review of an application generated with AI tools, focused on the security weaknesses those tools commonly introduce — exposed secrets, open database access, flawed authentication and unsafe AI features — followed by a prioritised plan to fix them. AI builders make it possible to ship in days; they do not guarantee the app is safe for real users’ data.

What we check

AreaWhat we look for
Secrets and keysAPI, payment and service keys exposed in the browser, repository or logs
Database accessSupabase row-level security, Firebase rules and API endpoints that expose other users’ data
AuthenticationSign-up, login, password reset, sessions, roles and rate limiting
AuthorisationWhether users can access or change records that are not theirs
Input handlingValidation, injection risks and file-upload handling, guided by the OWASP Top 10
AI featuresPrompt injection, data leakage through AI responses, and over-permissive AI tool access
DependenciesOutdated or vulnerable packages and hallucinated or unnecessary libraries
ConfigurationSecurity headers, HTTPS, CORS, error messages and environment separation

What you receive

  • Executive summary — the overall risk level in plain language, for founders and investors.
  • Prioritised findings — each issue rated by severity using CVSS-style scoring, with evidence.
  • Fix guidance — specific steps your team, your AI tool or our engineers can follow.
  • Retest — verification that critical and high-severity issues are resolved after fixes.

AI app security audit vs vulnerability testing

AI app security auditVulnerability testing
Best forApps built with AI builders and coding assistantsAny web app, mobile app, API or infrastructure
FocusThe specific failure patterns of AI-generated code, plus AI-feature risksBroad coverage across applications, servers and cloud
Includes code reviewYes — code, database rules and configurationPrimarily scanning and validation

When to get an audit

  • Before launching to paying users or storing personal data.
  • Before an investor due-diligence review or an enterprise customer’s security questionnaire.
  • After a period of rapid AI-assisted changes.
  • If you suspect keys or data may have been exposed.

How long does an audit take?

Most audits are completed within a few days to two weeks, depending on the size of the app, the number of user roles and integrations, and whether AI features are in scope.

After the audit

You can fix issues yourself using our guidance, or have our engineers do it. If the app needs more than security fixes to go live — hosting, backups, CI/CD and monitoring — see AI-built app to production.

Why Obii Kriationz

  • Security plus engineering — our testers work alongside developers, so recommendations are practical to implement.
  • Recognised standards — findings mapped to OWASP guidance and scored consistently.
  • Bengaluru-based since 2009 — 500+ technology projects delivered.

Work With Us

Is your AI-built app safe for real users?

Tell us about your app and how it was built. We will scope an audit around your launch timeline.

Request a Security Audit

How We Work

Our Process

01

Scoping

We agree the app, environments, user roles, integrations and AI features in scope, and how we will access them safely.

02

Code & Configuration Review

We review the codebase, database rules, environment variables and hosting configuration for common AI-generated weaknesses.

03

Hands-On Testing

We test authentication, authorisation, data access, inputs and AI features the way an attacker would — without harming live data.

04

Risk Prioritisation

We rate each finding by severity and business impact so you know exactly what to fix first.

05

Report & Walkthrough

We deliver an executive summary and technical findings, and walk your team through the fixes.

06

Retest

Once fixes are deployed, we verify that critical and high-severity issues are resolved.

Results That Matter

Client Outcomes

Know Your Real Risk

A clear, prioritised view of what could expose your users’ data — and what can safely wait.

Exposed Keys Closed

Secrets moved out of the browser and rotated before they are abused.

Data Locked Down

Database rules and authorisation fixed so users only ever see their own data.

Investor & Customer Ready

Evidence of a security review for due diligence and enterprise security questionnaires.

Safer AI Features

Prompt injection and data-leakage risks in AI features identified and mitigated.

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FAQ

Common
Questions

They can be, but AI-generated apps often ship with exposed API keys, misconfigured database rules, weak authentication and missing security headers. A security audit finds these issues before attackers or customers do.

Secrets and keys, database access rules such as Supabase row-level security or Firebase rules, authentication and authorisation, input handling against OWASP risks, dependencies, configuration and the security of any AI features.

Prompt injection is when a user crafts input that makes an AI feature ignore its instructions — for example revealing hidden data or performing actions it should not. We test AI features for this and recommend safeguards.

Most audits are completed within a few days to two weeks, depending on the size of the app, user roles, integrations and whether AI features are in scope.

No. We review code and configuration and test carefully, preferably on a staging copy, so live users and data are not affected. Fixes are applied only when you choose.

Yes. Our engineers can implement the fixes and retest, or your team can follow our guidance.

Vulnerability testing gives broad coverage across apps, APIs and infrastructure. The AI app security audit adds a code-level review focused on the failure patterns of AI-generated apps and the risks of AI features.