Review AI-generated website code as if it came from an unknown contributor: inspect the complete diff and its surrounding code, trace untrusted data to every sensitive sink, verify authentication and authorization on the server, check dependencies and secrets, and run layered security tests before deployment. A clean scanner result is a starting point, not proof that the application is safe.
What an AI-code security review must establish
Code produced by Copilot, ChatGPT, Cursor, or another coding agent can look polished while omitting a middleware check, weakening a cookie flag, inventing a package, or exposing a credential. The risk comes from the code’s behavior in your application, not from the fact that a model wrote it. OWASP’s DevSecOps guidance puts the standard plainly: “Generated code needs at least the same scrutiny as code from an unknown contributor.”
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Your review should answer four questions:
- What changed, and which trust boundaries does it cross?
- Can an unauthenticated or low-privilege user make the server perform an unauthorized action?
- Can attacker-controlled data reach an interpreter, browser sink, file system, internal network, log, prompt, or third-party service?
- What evidence shows that the change was tested, approved, and released with residual risk understood?
Start by defining the threat model. A public marketing site, a multi-tenant dashboard, and an application handling health or payment data need different controls. Record the data sensitivity, expected users, internet exposure, regulatory obligations, and the impact of account takeover or data disclosure. Do not describe a scan as “secure” without stating what it covered and what it could not observe.
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Prepare the review before reading individual functions
Inventory the generated change
List every generated or modified file, including files outside the main application directory:
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- Routes, controllers, middleware, server actions, API handlers, and WebSocket code.
- Templates, client components, browser scripts, static assets, and generated bundles.
- Database migrations, seed scripts, queue workers, cron jobs, upload handlers, and webhook consumers.
- Dependency manifests and lockfiles, build scripts, container files, infrastructure-as-code, and deployment manifests.
- CI workflows, reusable actions, test fixtures, documentation, and configuration files.
Identify the runtime and framework, public endpoints, authentication boundaries, data stores, queues, object storage, outbound HTTP clients, and administrative interfaces. Note whether the model or agent could read the repository, .env files, terminal output, issue text, pull requests, or deployment credentials. An entry in .gitignore does not prevent a local coding tool from reading a file that exists on disk.
Map trust boundaries
Draw a simple data-flow map from browsers and external APIs through the web server, middleware, business logic, databases, file storage, queues, logs, and third-party services. Mark where data changes privilege or interpretation: a browser value becoming a SQL parameter, a URL becoming a server-side fetch destination, or a database field becoming HTML. Those crossings determine where validation, authorization, encoding, and egress controls must be enforced.
A ten-step review workflow
1. Read the diff and its callers
Begin with the complete diff, not the model’s explanation. Look for removed checks, changed defaults, new routes, altered error handling, and configuration that silently broadens access. Then read the surrounding code: route registration, middleware order, callers, serializers, tests, environment handling, and deployment files. A function that validates input can still be unsafe if another route calls it without the validator, or if middleware is registered after the route.
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For each request parameter, header, cookie, uploaded file, message, database field, and webhook value, follow the value until it is used. Sinks include HTML and DOM insertion, template rendering, SQL or NoSQL queries, shell commands, file paths, redirects, outbound HTTP clients, logs, and prompts sent to an AI service.
Require schema validation and allowlists at trust boundaries. Use parameterized database APIs rather than concatenated query strings, fixed command arguments rather than shell interpolation, safe path-joining plus containment checks for files, and context-appropriate output encoding. Validation is not a substitute for encoding: a value safe in a database query may still be dangerous when inserted into HTML or a JavaScript string.
3. Test browser and server authorization independently
Do not treat a hidden button, disabled form control, or client-side route guard as authorization. Send direct requests to each sensitive endpoint while changing user IDs, tenant IDs, object IDs, roles, and HTTP methods. Verify authentication, object-level authorization, tenant isolation, administrator checks, and denial behavior on the server.
Check that authorization runs on every path, including bulk operations, exports, background jobs, GraphQL resolvers, WebSocket messages, and alternate content types. Look for insecure direct object references, predictable identifiers, confused-deputy behavior, and checks performed before a redirect but not after it. Confirm that failures do not reveal whether another account or object exists.
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4. Examine authentication, sessions, and request forgery defenses
Review registration, login, password reset, email verification, MFA enforcement, account recovery, logout, and session rotation. Tokens should be generated with a cryptographically secure source, expire appropriately, and be invalidated when risk events require it. Session cookies normally need Secure, HttpOnly, and an intentional SameSite setting; confirm the setting matches your cross-site use case.
Check CSRF defenses for cookie-authenticated state changes, origin or referer validation where appropriate, replay resistance for webhooks, rate limits on login and recovery, and consistent authorization on JSON and form endpoints. Test malformed, expired, duplicated, and concurrently used tokens rather than only the successful path.
5. Search for secrets and sensitive data leakage
Search the working tree, Git history, build output, source maps, browser bundles, logs, test fixtures, workflow variables, container layers, and generated artifacts for API keys, bearer tokens, passwords, private keys, database URLs, signing secrets, and personal data. Check whether debugging or error responses include credentials, stack traces, prompts, or full request bodies.
If a credential was exposed, remove it from code and rotate it at the issuer; deleting the line is not enough because history, caches, logs, and artifacts may retain it. Keep production secrets out of prompts and local terminal transcripts. Give coding agents only the files and permissions they need, and use short-lived credentials for CI.
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For every new package, action, container image, and provider, confirm the name, registry, maintainer or publisher, intended purpose, version, license, and provenance. AI tools sometimes suggest a plausible but nonexistent package or a similarly named typosquat. Require a lockfile or equivalent pinning and review install or post-install scripts.
Run the ecosystem’s dependency audit and check the current National Vulnerability Database, GitHub Advisory Database, or OSV records for affected versions. Inspect generated CORS rules, TLS verification, cookie settings, debug flags, IAM policies, firewall rules, storage visibility, and logging destinations. A vulnerable transitive dependency can remain exploitable even when your own source code is clean.
7. Review uploads, files, redirects, and outbound requests
For uploads, test size limits, content-type validation, filename normalization, decompression limits, archive extraction, storage permissions, malware handling, and whether uploaded content can execute. Prevent path traversal and ensure a resolved path remains inside the intended directory. Never trust a browser-supplied MIME type by itself.
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For redirects and server-side fetches, allowlist destinations or hosts, block loopback and private network ranges, restrict protocols and ports, limit response size and time, and disable unsafe redirect behavior. Check whether a user can reach cloud metadata endpoints or internal administration services through a URL parameter.
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Issue descriptions, pull requests, README files, dependency documentation, fetched web pages, generated comments, and tool descriptions can contain indirect prompt injection. An instruction hidden in those sources might persuade an agent to print secrets, weaken a test, alter a workflow, or approve its own change.
Use isolated runners, least-privilege tokens, restricted network egress, and ephemeral credentials. Review fork pull-request behavior carefully: untrusted code must not run with production secrets or write access to protected branches. Separate code generation from approval, require a human to review workflow changes, and prevent an agent from merging or releasing its own output.
9. Run layered security gates
On every pull request that contains AI-generated code, run the checks appropriate to the stack:
| Gate | Primary coverage | What it cannot prove |
|---|---|---|
| Static application security testing (SAST) | Unsafe data flows, injection patterns, weak cryptography, and some authorization mistakes in source | Runtime configuration, business intent, and paths the analyzer cannot model |
| Software composition analysis (SCA) | Known vulnerabilities, licenses, and dependency relationships | Malicious-but-undisclosed behavior, safe usage context, or vulnerabilities without a published advisory |
| Secret scanning | Credential-like strings in source, history, and artifacts | Secrets stored in external systems, transformed values, or credentials already used and rotated |
| Infrastructure-as-code scanning | Permissive network rules, public storage, broad IAM, and insecure defaults | Runtime drift and application-level authorization |
| Dynamic application security testing (DAST) | Exposed endpoints, headers, authentication behavior, and common runtime flaws in a deployed test target | Unreachable code, deep business workflows, and source-only defects |
| Interactive application security testing (IAST) | Runtime data flows observed while tests execute | Untested paths and production-only integrations |
OWASP’s AI security verification guidance lists SAST, IAST, DAST, secret scanning, infrastructure-as-code scanning, and SCA as appropriate pull-request controls. Configure failures according to risk, preserve reports with the commit, and document accepted exceptions. OWASP’s secure-review guidance warns: “Treat a clean scan as the start of review, not the end.”
10. Reproduce findings and obtain accountable approval
Manually validate high-confidence findings. Build a minimal request or test that demonstrates the issue, identify the affected asset and privilege level, and determine whether data can be read, changed, deleted, or exfiltrated. Review business invariants such as “a user may edit only their tenant’s records” or “a refund cannot exceed the captured amount.”
Record the generated prompt or task, model and tool identity, commit or diff, test environment, scan results, fixes, accepted risks, and reviewer. A qualified engineer or security reviewer should approve sensitive changes. Manual review complements automated testing because business logic, complex security controls, and context-specific vulnerabilities require human analysis.
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High-value web vulnerabilities to check deliberately
Cross-site scripting (XSS)
Search for unsafe HTML insertion, disabled template escaping, DOM sinks such as innerHTML, markdown rendered without sanitization, and user-controlled attributes or URLs. A value such as a profile name can become executable markup when inserted into a page without the correct context-aware encoding. Test stored, reflected, and DOM-based paths, including error pages and administrator views.
Injection beyond SQL
Inspect SQL and NoSQL query construction, shell commands, template expressions, LDAP filters, regular expressions, and outbound URLs. Prefer parameterized APIs and strict allowlists. Pay attention to “helpful” generated fallback code that switches to string concatenation when a parameter is missing.
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Broken access control
Look for missing route middleware, object-ID substitution, tenant-boundary failures, administrator endpoints exposed to ordinary users, and authorization performed only in browser code. Include asynchronous jobs and exports; a secure interactive page does not make a background endpoint secure.
Authentication and session defects
Check weak reset links, predictable tokens, insecure cookie attributes, missing CSRF defenses, absent MFA enforcement where policy requires it, login and recovery rate limits, and account-enumeration leaks. Verify that session identifiers rotate after login and privilege changes.
SSRF and unsafe fetches
Trace every user-controlled destination, redirect, webhook callback, image proxy, and URL preview. Test private-address blocking, DNS rebinding resistance, metadata endpoint protection, protocol restrictions, and outbound egress policy.
File and content handling
Review upload limits, archive extraction, path traversal, content-type confusion, public buckets, executable uploads, and content-security headers. Ensure downloads use safe names and do not expose internal paths.
Privacy, logging, and prompts
Check for tokens in logs or client bundles, excessive telemetry, sensitive data copied into prompts, and credentials inherited by CI or coding agents. Redact at collection boundaries and define retention for logs, traces, artifacts, and model transcripts.
Supply chain and configuration
Look for hallucinated or typosquatted packages, vulnerable versions, unpinned actions or images, permissive CORS, debug mode in production, disabled TLS verification, and overly broad IAM permissions. Verify that the deployed configuration matches the reviewed configuration.
Choosing a review approach
No single method covers source, dependencies, secrets, runtime, infrastructure, and business intent. Compare approaches by coverage, contextual reasoning, CI reproducibility, false-positive handling, audit evidence, data exposure to hosted services, and required privileges.
| Approach | Strengths | Gaps | Best use |
|---|---|---|---|
| Manual-only review | Strong context, workflow reasoning, and authorization analysis | Slow, inconsistent, and difficult to repeat for every change | Small sensitive changes and investigation of complex findings |
| Automated-only review | Fast, repeatable, and easy to enforce in CI | Misses business logic, context-specific authorization, and novel flaws; can produce noisy findings | Baseline gates on every pull request |
| AI-assisted review | Can summarize data flows, suggest tests, and triage large diffs | May hallucinate safety, inherit prompt injection, or receive sensitive code; its output still needs verification | Reviewer aid inside a least-privilege, isolated process |
| Layered workflow | Combines repeatable gates with human reasoning and recorded evidence | Requires pipeline integration, skilled reviewers, and clear ownership | Default for production-bound AI-generated code |
Practical checks you can automate in a pull request
Use the commands and tools native to your stack, and run them against the exact revision being reviewed. Typical examples include:
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npm audit,pip-audit, or the equivalent for the project’s package manager. - Secret scanning across the current tree and Git history, with a verified rotation process for any match.
- SAST and IaC scanners configured to fail on agreed high-severity findings, while preserving lower-severity results for triage.
- DAST or IAST against an isolated environment populated with non-production data and test credentials.
Do not copy commands blindly into production pipelines. Confirm the scanner’s scope, ignore rules, credential permissions, network access, and report retention. An ignored path or suppressed finding is a security decision that should have an owner and an expiry date.
How to triage findings and residual risk
Prioritize exploitability and impact
Escalate issues that permit unauthenticated remote code execution, credential theft, cross-tenant access, sensitive-data disclosure, account takeover, or supply-chain compromise. A lower-severity bug in an internet-facing administrative endpoint may deserve faster treatment than a higher-scoring issue unreachable in the deployed configuration.
Separate true positives from noise
Reproduce the path, identify required privileges and preconditions, and inspect the deployed configuration. If a finding is not exploitable, document why rather than deleting it. If the code is unreachable today but could become reachable after a route change, record that assumption and add a regression test or guard.
Make exceptions explicit
Every accepted risk should state the affected component, rationale, compensating control, owner, expiration or review date, and conditions that would trigger reopening. Never suppress a scanner rule merely because generated code is difficult to change.
Release checklist for AI-generated website code
- The generated diff and surrounding call paths were reviewed by a human.
- Trust boundaries, public endpoints, data stores, queues, uploads, webhooks, and agent permissions are documented.
- Input-to-sink tracing covers HTML, DOM, queries, commands, paths, redirects, outbound requests, logs, and prompts.
- Server-side authentication, authorization, tenant isolation, CSRF defenses, sessions, and rate limits were tested with direct requests.
- Secrets were scanned in source, history, artifacts, logs, and CI; any exposure was rotated.
- Dependencies, actions, images, versions, install scripts, and provenance were verified.
- SAST, SCA, secret, IaC, and applicable DAST or IAST checks ran on the reviewed commit.
- High-confidence findings were reproduced, business-logic invariants were tested, and exceptions have owners.
- CI and agent workflows use least privilege, isolated runners, restricted egress, and ephemeral credentials.
- An accountable engineer or security reviewer approved the change with evidence retained.
This layered process is the practical answer to trusting AI-generated website code: trust the evidence and controls around the change, not the apparent confidence of the generated explanation.
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