AI-Generated Code Audit: Security & Architecture Checklist
AI-Generated Code Audit: What a Security and Architecture Review Should Cover
An AI-generated code audit is a security and architecture review of a codebase built with tools like Lovable, Cursor, Bolt, Replit Agent, v0, Claude Code or Copilot. A useful audit combines automated scanning with manual review of authentication, authorization, secrets, data access, APIs, dependencies, architecture, scalability, tests, deployment and monitoring. It ranks every finding by real risk and ends with a clear decision: launch, fix, refactor or rebuild.
If you have a working app with real users, a database, payments and a lot of AI-written code, the question is rarely "does the demo work?" It is whether you understand the security, architecture and operational risk well enough to keep building on it.
This guide publishes the methodology we use. You can run a basic version yourself with it, and you will know what to expect if you hire someone to do it.
What Is an AI-Generated Code Audit?
DefinitionAn AI-generated code audit is a structured review of a codebase built with AI-assisted tools. It looks for security vulnerabilities, authorization problems, exposed secrets, unsafe data access, dependency risk, architecture and scalability limits, missing tests, operational gaps, and maintainability problems, then ranks each finding by real risk so the team can decide what to fix first.
The goal is not to work out which lines a model wrote. The goal is to decide whether the code is safe, understandable, maintainable and fit for production. Human-written code gets the same review against the same standards, such as the OWASP Top 10:2025 and the OWASP Application Security Verification Standard (ASVS).
Is AI-Generated Code Secure?
Short answerAI-generated code can be secure, and it can be insecure. Research shows models often pick an insecure option when a task leaves room for one, and AI-assisted commits can add technical debt that lingers. The practical answer is to hold AI-written code to the same production standards as any other code and review it before real users depend on it.
Here is what the current evidence says, what we take from it, and what it means for an audit.
Research finding
Veracode's 2025 GenAI Code Security Report gave 80 code-completion tasks to more than 100 LLMs across Java, JavaScript, C# and Python. Each task was a single function covering one of four CWE types with a secure and an insecure way to finish it. Veracode's static analyzer found an OWASP Top 10 flaw in 45% of tasks. Java failed more than 70% of the time. Newer, larger models wrote more functional code but were not more secure.
Limitations
These were short, isolated functions, judged by one vendor's static analysis, with no human-written baseline and no human review step. They do not measure full applications. A 2026 preprint, Debt Behind the AI Boom (Liu et al.), tracked AI-attributed code in 6,000+ public GitHub repositories across Copilot, Claude, Cursor, Gemini and Devin. In every tool, at least 15% of commits introduced a technical-debt issue, and many persisted. It is not yet peer-reviewed and relies on attribution rules.
Our interpretation
Neither study says AI code is unsafe by nature. Both say that code accepted without review carries predictable risk, and that the risk grows with speed. In AI-assisted projects, more code and more architectural choices tend to land before anyone reviews them. That is the gap an audit closes.
Practical audit recommendation: do not rely on a percentage from any study to judge your app. Review your own authorization model, secrets, data access and dependencies directly, using the checklist below.
Does AI-Generated Code Need a Different Kind of Audit?
The security standards are the same. What changes is where reviewers spend extra time. AI-assisted codebases often grow through many separate prompts and sessions, so a reviewer looks harder at:
Repeated generated patterns and duplicated logic that drift apart over time
Inconsistent conventions between sessions or tools
Invented, deprecated or misused APIs and libraries
Dead code and unused dependencies left behind by abandoned attempts
Security checks that live only in the browser
Temporary workarounds that quietly became permanent
Architecture drift, where no one decided the structure on purpose
Code volume that outpaced review
None of these show up in every AI-built app. Many teams using Cursor or Claude Code with strong review habits ship clean code. The list tells a reviewer where to look first.
Automated Scanning vs Manual Code Review
Short answerAutomated tools are fast and consistent at finding known patterns: vulnerable dependencies, committed secrets, risky function calls. Manual review is needed for anything that depends on intent, such as whether a user should be allowed to see a record, whether the architecture fits the business, or whether an important check is missing. A serious audit uses both.
Review typeGood atWeak at Static analysis (SAST)Injection patterns, unsafe functions, risky data flows inside the codeBusiness logic, missing checks, cross-service context; produces false positives Dependency scanning (SCA)Known CVEs in packages, outdated versions, license flagsWhether the vulnerable code path is reachable; brand-new malicious packages with no CVE Secret scanningKeys and tokens in code and Git historyTelling a public publishable key from a privileged secret; secrets in third-party dashboards Automated testsProving known behavior still works after a changeRisks nobody wrote a test for Manual security reviewAuthorization intent, trust boundaries, exploitability of a findingSpeed and breadth on large codebases Architecture reviewStructure, coupling, failure modes, scaling limitsLine-level bugs Business-logic reviewPricing integrity, workflow rules, tenant isolation, missing logicNeeds product context from the team
Why not just run a security scanner?
Scanners catch known dependency vulnerabilities, obvious secret exposure and common code patterns. They regularly miss broken business authorization, tenant-isolation mistakes, missing business rules, wrong trust boundaries and scalability problems, because none of those are a pattern in a single file. They also flag issues that are not exploitable in context. A clean scanner score is a starting point, not a verdict.
How to Audit AI-Generated Code: The 24-Point Checklist
Short answerStart by mapping the system, then review each area in order: architecture, authentication, authorization, data access, secrets, APIs, input validation, dependencies, storage, payments, error handling, logging, privacy, code quality, tests, performance, scalability, CI/CD, configuration, backups, monitoring, compliance gaps and ownership. Record each finding with evidence and a severity.
Step 1: Inventory the system first
You cannot judge security without knowing where the boundaries are. Before reading code, list the frontend, backend, database, authentication provider, APIs, storage, payment processor, AI/model APIs, other third-party services, background jobs, webhooks, deployment platform, and every environment (production, staging, preview). Each line on that list is a place where data crosses a trust boundary, and each boundary needs a check.
#AreaWhat to checkWhy it mattersCommon red flag 01Application inventoryAll services, environments, integrations, ownersUnlisted systems go unreviewedA webhook or cron job nobody remembers adding 02ArchitectureClient/server split, data flow, trust boundariesWrong boundaries make every later fix harderBusiness rules enforced only in the UI 03AuthenticationSignup, login, reset, sessions, token storage, expiryConfirms who a user isSessions that never expire 04AuthorizationRoles, ownership, tenant isolation, admin actionsBroken Access Control is #1 in OWASP Top 10:2025Admin pages hidden in the UI but open on the server 05Database and data accessPolicies, grants, privileged roles, constraints, migrationsData exposure usually starts hereTables reachable from the client with no access policy 06Secrets and credentialsPrivileged keys in code, bundles, logs, Git historyOne leaked secret can bypass every other controlA service-role or Stripe secret key in frontend code 07API securityObject-level access, rate limits, error responsesAPIs expose data directlyChanging an ID in a request returns someone else's record 08Input validationServer-side types, ranges, allowed values, filesBrowser checks can be skippedPrice or quantity accepted from the client as-is 09Dependencies / supply chainKnown CVEs, abandoned or nonexistent packages, lockfilesSupply chain is #3 in OWASP Top 10:2025No lockfile; packages nobody can explain 10File / storage securityPublic vs private buckets, upload rules, signed URLsUploaded documents are often sensitiveID documents in a public bucket 11PaymentsServer-side verification, webhook signatures, idempotencyMoney errors are expensive and visibleOrder marked paid from a browser redirect alone 12Error handlingFailure paths, safe defaults, user-facing messagesMishandled exceptions is new at #10 in 2025Stack traces or SQL errors shown to users 13LoggingWhat is logged, where, and who can read itNeeded for investigation; can leak dataPasswords or tokens written to logs 14Data privacyPersonal data collected, retained, exported, deletedDrives legal and customer trust exposureNo way to delete a user's data 15Code qualityDuplication, dead code, huge files, namingMessy code makes security fixes slow and riskyThe same permission check copied in 12 places 16Test coverageTests on auth, data isolation, payments, core flowsPrevents silent regressionsNo tests at all on authorization 17PerformanceSlow queries, missing indexes, N+1 calls, payload sizeSlowness at launch is a real outageUnbounded queries that load every row 18ScalabilityBehavior at 10× current usage, third-party limits, costShows what breaks first as you growOne AI call per page view with no cap 19CI/CDBuild pipeline, checks before deploy, who can deployControls what reaches productionAnyone can push straight to production 20Environment configurationEnv variables, debug flags, CORS, separate environmentsMisconfiguration is #2 in OWASP Top 10:2025Production pointing at a test database, or debug mode on 21Backups / recoveryBackups exist, restores tested, rollback worksTurns an incident into an inconvenienceBackups enabled but never restored 22Monitoring / observabilityError tracking, alerts, job and integration failuresYou cannot fix what you cannot seeUsers report errors before the team knows 23Compliance gap flagsAudit logs, encryption, accessibility, data residencyShows readiness gaps before a customer asksHealth data handled with no access logging 24Documentation / ownershipArchitecture notes, runbooks, who owns each integrationCode nobody understands cannot be maintainedThe only person who knows the payment flow has left
Security Review: Authentication, Authorization and Data Access
Short answerCheck that users are who they claim to be, that every request is limited to data and actions that user is allowed, and that those limits are enforced on the server or database, never only in the browser. Most serious findings in rapidly built apps sit here, which matches OWASP ranking Broken Access Control first.
OWASP Top 10:2025 categoryWhat to check in an AI-assisted codebase A01 Broken Access ControlOwnership checks on every read and write; tenant isolation; admin routes protected on the server A02 Security MisconfigurationDatabase policies, storage buckets, CORS, debug settings, default credentials A03 Software Supply Chain FailuresLockfiles, package provenance, nonexistent or abandoned packages A04 Cryptographic FailuresEncryption in transit and at rest, password hashing, token signing A05 InjectionParameterized queries, output encoding, prompt input passed into queries A06 Insecure DesignTrust assumptions baked into the architecture A07 Authentication FailuresSession expiry, reset flows, rate limits on login, MFA where risk warrants A08 Software or Data Integrity FailuresWebhook signature checks, unsigned updates, CI pipeline integrity A09 Security Logging & Alerting FailuresSecurity events logged and alerted, without sensitive data in logs A10 Mishandling of Exceptional ConditionsFail-closed behavior, error paths, partial failures in integrations
Authentication review
Authentication answers one question: who are you? Review signup, login, logout, password reset, email verification where it matters, session management, where tokens are stored, how sessions expire, OAuth configuration if used, and MFA for admin or high-risk accounts. A common gap is a login screen that looks complete while the server never validates the session it receives.
Authorization and data-access review
Authorization answers a different question: what are you allowed to do? Check user-to-user data isolation, organization or tenant isolation, role permissions, admin privileges, server-side checks, database-level policies, API authorization and object ownership. Defensive red flags to look for:
A user's role can be changed in frontend state and the server accepts it
Admin protection exists only because the menu item is hidden
Changing a record ID in the URL or request shows another user's data
Database policies allow any signed-in user to read every row
The simplest test uses two accounts you own: sign in as User A and confirm you cannot read or change anything that belongs to User B.
Database security
Review access policies, grants, any use of privileged roles, constraints, relationships, migration history, indexes, where sensitive data lives, delete behavior and tenant boundaries. For Supabase apps, this means Row Level Security. Supabase's own API key documentation explains that the publishable key only reaches what RLS allows, while secret and service_role keys bypass RLS entirely. So RLS should be enabled on every exposed table, with policies reviewed. Other stacks enforce access in a server layer instead. RLS is one valid pattern, not a universal requirement; what matters is that access is enforced somewhere the user cannot change.
Secrets and Credentials Check
Short answerSearch the code, the built frontend bundle, configuration and the full Git history for privileged credentials: database passwords, service-role or secret API keys, Stripe secret keys, AI provider keys, SMTP and cloud credentials, and signing secrets. Keys designed to be public are not leaks. Privileged keys anywhere outside a trusted server are.
The distinction matters. A Supabase publishable key or a Stripe publishable key is meant to ship in the browser. Calling it a leaked secret creates noise. A Supabase secret key, an OpenAI key or a Stripe secret key in frontend code is a real finding, usually Critical.
If you find a real secret
Revoke or rotate it first
Remove it from client code
Store the new value only in the trusted server environment
Review provider logs and account activity
Check repository history and other copies
Update every system that used the old key
Investigate any sign of misuse
Deleting the line in a new commit does not remove the old value from Git history or from anyone's clone. GitHub's guidance is to rotate the credential as the first step for exactly that reason.
API Security and Input Validation
Review authentication and authorization on every endpoint, rate limiting where abuse is possible, input validation, error responses, object-level access, mass assignment, webhook verification, replay protection where relevant, and any server logic that trusts what the client sends. The OWASP API Security Top 10 (2023) is a good map: its top risks are broken object-level authorization (one user reaching another's object), broken object-property-level authorization (editing a field like role or plan they should not touch), and broken function-level authorization.
Never trust the client
Frontend validation improves the user experience. It is not a security control, because anyone can send a request without your UI. Validate critical inputs on the backend or in the database: types, ranges, allowed values, file types and sizes, ownership and business rules. Prices, discounts, quantities and roles should always be computed or checked on the server.
Dependency and Supply-Chain Review
Check for known vulnerabilities, abandoned packages, unnecessary or duplicate libraries, outdated versions, package provenance, a committed lockfile and automated update tooling. An outdated package is not automatically a vulnerability; rank each one by whether a known issue affects code your app actually uses.
Hallucinated or questionable dependencies
AI tools sometimes reference packages that do not exist. In a USENIX Security 2025 study, Spracklen et al. generated 576,000 Python and JavaScript samples from 16 models and found 19.7% of recommended packages did not exist, with commercial models far lower (about 5%) than open-source ones (about 22%). Attackers can register those names. The study used 2024-era models and generated prompts, so treat it as a reason to check, not a prediction for your repo. Confirm that every package is real, maintained and intended, and look for deprecated libraries, incorrect API calls, outdated syntax and imports nothing uses.
Payments and File Storage
If the app takes payments, confirm secret keys stay server-side, payment status is verified on the server, webhook signatures are checked, duplicate events are handled with idempotency, and subscription state, refunds, failed payments and pricing cannot be altered from the client. Card data should go to the processor, never your database.
For storage, review public versus private buckets, access policies, upload validation, signed URL expiry, ownership rules, how sensitive documents are handled, executable file risks and retention.
What Should an Architecture Review Cover?
Short answerA software architecture review checks whether the system's structure fits what the business needs now and in the next stage. It covers component boundaries, data flow, trust boundaries, state, database design, third-party dependencies, failure modes, maintainability and realistic scaling bottlenecks. It does not assume more complexity is better.
AreaQuestionRed flag Component boundariesWhat belongs in the client and what on the server?Pricing or permissions computed in the browser Data flowWhere does user and business data travel?Personal data sent to services nobody listed Trust boundariesWhich users and systems are trusted to do what?Every backend function runs with full privileges State managementWhere is important state stored and who can change it?Order status held only in local storage Database designAre relationships and constraints explicit?Core data stored as loose JSON with no constraints Third-party dependenciesWhat external systems does the app rely on?A core flow fails silently if one API is down Failure modesWhat happens when a dependency fails or times out?No retries, no timeouts, no user message MaintainabilityCan a new engineer extend it safely?Competing patterns for the same job ScalabilityWhere are the realistic bottlenecks?Work done per request that belongs in a background job
A well-organized monolith is often the right architecture for an early-stage product. A review should never recommend microservices by default.
Architecture debt
Architecture debt is the accumulated cost of early structural decisions that make future changes harder, riskier or slower. Look for business logic inside UI components, authentication logic scattered across files, multiple competing data models, database access from places that should not have it, no clear module boundaries, heavy duplication, and shortcuts that became permanent.
Code Quality Checks for AI-Assisted Codebases
Review duplicated code, very large components or functions, dead code, hard-coded values, repeated business logic, inconsistent error handling, conflicting patterns, poor naming, missing type safety where the stack expects it, tight coupling and unclear ownership. Code quality is not a style preference. When a permission check is copied into twelve files, fixing a security bug means finding all twelve.
Performance and Scalability Review
Check realistic early-production risks: slow queries, missing indexes, N+1 queries, repeated API calls, large payloads, unbounded queries, excessive AI/model calls, image and file handling, background job design, rate limits, concurrency and caching where it helps.
Scalability review asks one question: what happens when usage grows 10× from today? Look at the database, APIs, third-party limits, queues and jobs, storage, AI usage, cost growth and concurrency. "Scalable" means nothing until you name the load. Design for the next realistic stage, not for millions of hypothetical users.
What Tests Should Exist Before Production?
Focus on risk. Before launch, you want tests on authentication, authorization, data isolation between users and tenants, payments, critical business workflows, API behavior, error paths, and regression tests for any serious bug you have already fixed. Add integration tests at system boundaries and end-to-end tests for the few flows that make money. A codebase can have 100% line coverage and still miss the one authorization check that matters. Targeted tests on critical workflows usually give more protection than a coverage target.
Operations: CI/CD, Observability, Backups and Recovery
CI/CD and deployment. Confirm there is a protected production branch, environment variables are managed outside the code, builds run automated checks, a staging environment exists where the risk justifies it, deployment permissions are limited, database migrations are reviewed, and a bad deploy can be rolled back. A small startup does not need enterprise DevOps. It does need a repeatable path to production.
Logging and observability. Check error tracking, structured logs, audit logs where required, performance monitoring, and alerts for failed background jobs, API failures and integration failures. Also check that logs do not contain passwords, tokens or private customer data.
Can the application recover from failure? Confirm database backups exist, a restore has actually been tested, data can be exported, deployments and migrations can be rolled back, and the team agrees on recovery expectations. A backup that has never been restored is an assumption.
What Does "Compliance Gap" Mean in a Code Audit?
An engineering audit can flag technical areas that matter for HIPAA, SOC 2, GDPR, PCI DSS responsibilities, WCAG accessibility or industry data rules: missing audit logs, unencrypted sensitive fields, no data-deletion path, inaccessible forms. The audit can identify technical gaps that may affect compliance readiness, but formal legal or compliance determination requires the appropriate qualified professional. A code audit is not a SOC 2 audit, a HIPAA certification or a GDPR legal opinion.
How Should Code Audit Findings Be Ranked?
Short answer Rank each finding by severity (how bad the impact could be), likelihood (how easily it could happen) and priority (how soon to fix it). A common scale is Critical, High, Medium and Low. Context decides the rating: the same missing check is Critical on a table of medical records and Low on a table of public blog posts.
Severity General meaning Example Critical Immediate, severe security or data risk One user can read another user's private records High Significant security or operational risk A privileged action runs without a server-side authorization check Medium Important, less immediate No alerting on failed payment webhooks Low Limited impact or maintain ability Minor configuration drift or duplicated helper code
This is an example framework, not a universal standard. Keep the three ideas separate:
Severity is how bad the impact could be.
Likelihood is how easily or how often the problem could be triggered.
Priority is how soon the team should fix it, given both.
Example audit finding
Finding
User records accessible across accounts
Severity
Critical
Evidence
Two authorized test accounts demonstrated cross-account read access
Impact
Private user information could be exposed
Recommended fix
Enforce authorization at the server and database layer
Retest required
Yes
What the executive summary should tell a founder or investor
It should answer, in plain language: Can we launch? What would block launch? What is high risk? What is maintainability debt? What can wait? What should be fixed first? Does this need hardening, refactoring, a partial rebuild or a full rebuild? What is the likely remediation scope? A summary that is a dump of scanner warnings has not done its job.
Fix, Harden, Refactor, or Rebuild?
Fix: correct a localized issue.
Harden: strengthen security or reliability without major structural change.
Refactor: change internal structure while keeping expected behavior.
Partial rebuild: replace one problematic subsystem.
Full rebuild: recreate most or all of the architecture.
Finding pattern Likely direction A few RLS or authorization gaps Fix and harden Exposed privileged secrets Rotate, then fix Heavy duplication, inconsistent patterns Refactor Weak but salvageable architecture Refactor and harden One unsafe subsystem (for example payments or file handling)Partial rebuild Architecture fundamentally conflicts with requirements Consider a rebuild
Who wrote the code is not a reason to rebuild. Most AI-assisted apps we see can be fixed and hardened. When the evidence does point to a rebuild, it becomes a custom software development project scoped from the audit findings.
Is a Code Audit the Same as a Penetration Test?
No. A code, security and architecture audit reviews source code, configuration, architecture, policies, dependencies and implementation from the inside. A penetration test focuses on actively testing a running system for exploitable behavior from an attacker's point of view. They find different things. Apps handling health, financial or other high-risk data may need both.
30-Minute Founder / CTO Self-Audit
Answer these honestly. Any "no" or "not sure" is where a deeper review should start.
Can you draw your architecture and every integration on one page?
Signed in as User A, can you see or change anything owned by User B?
Are all privileged secrets on the server, and none in the frontend bundle or Git history?
Is admin authorization enforced on the server, not just hidden in the UI?
Do you know every dependency and why it is there?
Are login, data isolation and payments covered by tests?
Do errors reach a tool that alerts someone?
Do you have backups, and have you ever restored one?
Can you roll back a bad deployment in minutes?
Do you know what breaks first at 10× today's traffic?
Does someone on the team understand every critical integration?
When Is an Independent Audit Worth It?
Many apps do not need a paid audit. A small internal tool with a handful of trusted users may only need the self-audit above. An independent review is worth it when the stakes rise:
Real users or customers are about to onboard
Payments are going live
The app stores sensitive personal, financial or health data
Multiple organizations share one database
AI generated a large share of the code, quickly
The original developer has left
Nobody can clearly explain the authorization model
Usage is growing past prototype levels
Enterprise customers are asking compliance questions
Recurring bugs suggest deeper architecture problems
Investor due diligence is approaching
Why investors may want an independent code review
Investors doing technical due diligence usually want to know: Does the company own and understand its code? Could another engineering team maintain it? Are critical security risks hidden? Does the architecture fit the business? Will scaling force a rewrite? Is a core system dependent on one founder or one tool? Not every round needs a full audit, but a plain-language report answers these questions with evidence.
How Long Does an AI-Generated Code Audit Take?
Short answer It depends on scope: codebase size, number of services, integrations, authentication complexity, database design, risk level and how well the system is documented. A small single-app review can take days; a multi-service platform takes longer. Techallin's Vibe to Production Assess review is currently delivered in 3–5 business days.
That figure is our offering, not an industry benchmark. Missing documentation and many integrations are the most common reasons any audit takes longer.
How Much Does an AI-Generated Code Audit Cost?
Short answerCost depends on codebase size, number of services, complexity, security risk, integrations, database design, depth of architecture review, documentation, reporting and whether retesting is included. Techallin's current Vibe to Production Assess offering is priced at $1,500–$2,500 fixed, based on scope, and the fee is credited toward any Fix or Build engagement.
When comparing quotes, ask what is included: manual review or scanner output only, architecture review, a severity-rated report, an executive summary, and retesting after fixes.
What Happens After an AI Code Audit?
The audit's job is to turn worry into a decision backed by evidence. Typical outcomes:
Launch with minor fixes. Nothing blocks launch; a short list gets cleaned up.
Fix and harden. Critical and High issues are fixed first, then testing and deployment are tightened.
Refactor selected areas. Duplicated or tangled code is restructured where it slows safe changes.
Partially rebuild a subsystem. One component is replaced while the rest stays.
Full rebuild. Only when the architecture cannot support the requirements.
Defer non-critical debt. Low-priority items go on a tracked list instead of delaying launch.
How Techallin's Vibe to Production Assess Works
Techallin's vibe-coded app audit and rescue service, Vibe to Production, starts with two separate steps.
Free 30-minute assessment call. You share your app, stack and concerns. We do a quick surface-level review and tell you whether a full audit makes sense, or whether you are in better shape than you think.
Vibe to Production — Assess (paid audit). $1,500–$2,500 fixed fee, 3–5 business days, with read access to your repository. It includes:
Automated and manual security scanning
Architecture and scalability review
Secrets and API key exposure check
Authentication and data-access audit
Compliance gap flagging (HIPAA, SOC 2, GDPR, ADA)
Severity-rated findings report (Critical / High / Medium / Low)
Fix-or-rebuild verdict with hour estimates
Plain-language executive summary
If you want us to do the work, we define a fixed-scope remediation engagement from the findings. Fix & Harden runs $5K–$15K over 2–6 weeks; Full Build runs $15K–$25K+ over 4–8 weeks. The Assess fee is credited toward either. Techallin does not offer penetration testing as part of Assess.
Not sure whether you need a few fixes, a refactor or a rebuild?
Vibe to Production Assess reviews security, architecture, data access, secrets, scalability and production risk before you commit to remediation. Start with a free 30-minute assessment call. If you don't need an audit, we'll tell you.
Frequently Asked Questions
What should an AI-generated code audit include?
It should include an inventory of the system, automated scanning (static analysis, dependency and secret scanning) and manual review of authentication, authorization, database access, secrets, APIs, input validation, storage, payments, architecture, scalability, tests, CI/CD, logging, backups and monitoring. Each finding should come with evidence, a severity rating and a recommended fix. The report should end with an executive summary that says whether you can launch and whether the right next step is fixing, refactoring or rebuilding.
Is AI-generated code less secure than human-written code?
Not by definition. Veracode's 2025 study found models introduced an OWASP Top 10 flaw in 45% of 80 single-function tasks, but it tested isolated snippets without human review and had no human baseline. Human code has the same vulnerability classes. The practical difference is speed: AI-assisted teams can produce more code than they review. Judge your codebase by reviewing it, not by a headline percentage.
How do you audit AI-generated code?
Map the system and its trust boundaries first. Run automated scanners for known patterns, dependencies and secrets. Then manually review authentication, authorization and data access, using two test accounts to confirm users cannot reach each other's data. Review architecture, scalability, tests and operations. Record each finding with evidence and a severity, then group findings into a fix, refactor or rebuild recommendation.
What security checks should an AI code audit perform?
At minimum: access control and tenant isolation, authentication and session handling, privileged secrets in code and Git history, database policies such as Row Level Security, API object-level authorization, server-side input validation, dependency and supply-chain risk, storage permissions, payment verification and webhook signatures, error handling, and logging that does not leak sensitive data. The OWASP Top 10:2025 and OWASP API Security Top 10 are useful maps.
Does a code audit include architecture review?
A thorough one should. Many costly problems are structural rather than line-level: business rules in the browser, unclear trust boundaries, competing data models or missing failure handling. Architecture review explains why certain bugs keep appearing and whether the system can support the next stage of growth. Techallin's Assess includes architecture and scalability review.
Can automated scanners audit AI code on their own?
No. Scanners are good at known patterns, vulnerable packages and committed secrets, and every audit should use them. They miss broken business authorization, tenant-isolation errors, missing logic, wrong trust boundaries and scaling problems, and they produce false positives. Manual review is needed to decide which findings are real and exploitable in your context.
What is the difference between a code audit and a penetration test?
A code audit reviews the system from the inside: source code, configuration, architecture, policies and dependencies. A penetration test probes a running system from the outside to find exploitable behavior. An audit explains why a weakness exists and how to fix it structurally; a pen test proves what an attacker could do. High-risk systems may need both.
How long does an AI code audit take?
It depends on codebase size, number of services, integrations, authentication complexity, risk level and documentation. There is no single industry standard. Techallin's Vibe to Production Assess is currently delivered in 3–5 business days.
How much does an AI-generated code audit cost?
Price depends on scope: size, services, integrations, risk, depth of architecture review, reporting and retesting. Techallin's Vibe to Production Assess is currently $1,500–$2,500 fixed, based on scope, and the fee is credited toward any Fix & Harden or Full Build engagement. Before that, you can book a free 30-minute assessment call to see whether an audit is worth it.
What happens after an audit?
You get a prioritized list and a recommendation. Outcomes range from launching with minor fixes, to fixing and hardening, refactoring selected areas, partially rebuilding one subsystem, or, rarely, a full rebuild. Low-priority debt can be deferred and tracked. You can do the work in-house or ask the auditor to scope a fixed remediation engagement.
Can AI-generated code be fixed without rebuilding?
Usually, yes. Authorization gaps, exposed secrets, missing database policies, missing tests and weak deployment practices can almost always be fixed in place. A rebuild is justified when the architecture fundamentally conflicts with the product's requirements, not because an AI tool wrote the code.
When should a founder get an independent code audit?
Before real users, payments or sensitive data go live; when multiple customers share a database; when the original developer has left; when nobody can explain the authorization model; when growth exposes recurring bugs; and before investor due diligence. A small internal tool with trusted users may only need the self-audit checklist in this guide.
Sources
We audit, fix, and harden AI-built apps for production.