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Review AI-generated code as if it came from an unfamiliar contributor: understand the complete change, test its security and accessibility behavior, and require a human owner before merging. AI assistance can accelerate implementation, but it does not transfer responsibility for correctness, maintenance, or compliance.
Can you trust AI-generated code?
Not by default. Generated code may compile, pass its accompanying tests, and still contain a missing authorization check, unsafe input handling, an exploitable dependency, or an inaccessible interaction. The correct question is not whether a model is trustworthy; it is whether this specific change has been understood, tested, and accepted by an accountable human.
The OWASP DevSecOps Guideline puts the standard plainly: “Generated code needs at least the same scrutiny as code from an unknown contributor.” Its Secure Coding with AI Cheat Sheet adds: “AI-generated code must have a human owner.” Treat those statements as release criteria, not general advice.
This workflow is intentionally technology-neutral. Adapt the checks to your language, framework, deployment model, threat model, and regulatory obligations. The accessibility examples below address web content and use WCAG 2.2 as the reference point; WCAG alone does not cover every native application or legal requirement.
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1. Establish scope and intended behavior
Begin before examining individual lines. Read the ticket, design note, acceptance criteria, issue discussion, and any prompt or specification used to generate the code. Then inspect the entire pull request, not only the files that appear to contain the feature.
Read the complete change set
- Application code, templates, styles, database migrations, and API schemas.
- Dependency manifests and lockfiles, including transitive version changes.
- Unit, integration, end-to-end, security, and accessibility tests.
- Configuration, environment handling, feature flags, scripts, containers, CI workflows, and deployment files.
- Documentation and generated artifacts that may alter build or release behavior.
Compare every edit with the requested behavior. Unrelated file changes, unexplained permissions, new network calls, disabled checks, or an unexpected dependency are reasons to pause and ask for an explanation. A small-looking prompt can produce a broad diff, especially when an agent is allowed to modify configuration or run repository tools.
Account for untrusted external context
AI agents can process issue bodies, pull-request comments, README files, logs, fetched pages, and other content that an attacker may have influenced. OWASP describes this as an indirect prompt-injection risk. Inspect commands, files, and permissions after an agent has handled external content; do not assume that a plausible instruction in a comment belongs to the project owner.
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Write down the expected outcome
State what the change should do, what it must not do, the data it may access, and which users or services may invoke it. A reviewer who cannot summarize the intended behavior in a few precise sentences is not ready to approve the implementation.
2. Increase scrutiny when a change crosses a risk boundary
Review depth should follow impact, not the amount of generated text. Route high-risk changes to a security champion or another reviewer with appropriate training, even when the diff is short.
| Risk trigger | Questions that require deeper review |
|---|---|
| Authentication and sessions | Are credentials, tokens, session rotation, expiration, recovery, and logout handled consistently? Can an unauthenticated or partially authenticated user reach the operation? |
| Authorization and tenancy | Is permission checked at every protected operation, using the server-side identity and resource ownership rather than a client-supplied identifier? |
| Input parsing and deserialization | What formats are accepted, how are limits enforced, and can malformed or hostile input trigger code execution, resource exhaustion, or unexpected type conversions? |
| Uploads and file processing | Are type, size, storage location, filename, decompression, and downstream processing restricted? Can an uploaded object become executable or public? |
| Cryptography and secrets | Are approved, maintained APIs used with suitable key storage, algorithms, randomness, rotation, and error handling? Are secrets absent from source, logs, tests, and client bundles? |
| Public endpoints and integrations | Are rate limits, authentication, replay protection, outbound requests, webhooks, CORS, and third-party trust assumptions explicit? |
| New data stores or network exposure | What data is retained, who can query it, how is it isolated, and which ports, routes, services, or credentials become reachable? |
Changes in these areas deserve threat-focused tests and, where appropriate, an independent review. A routine formatting change does not justify the same process as a new authorization path.
3. Trace security behavior from input to outcome
Generated code often looks locally reasonable while violating an invariant across several functions. Follow each untrusted value from its entry point to every use, and follow each security decision to the operation it is supposed to protect.
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Validate and constrain input
- Identify all sources: request parameters, headers, cookies, uploaded files, messages, environment variables, database records, and third-party responses.
- Check type, length, encoding, range, allowed values, and nesting limits at a trusted boundary.
- Prefer structured query and command APIs over string-built queries or shell commands.
- Encode output for its actual context, such as HTML, an attribute, a URL, JavaScript, or a log entry.
- Reject malformed input safely and ensure errors do not disclose credentials, internal paths, tokens, or sensitive records.
Do not accept a validation function merely because it exists. Confirm that the validated value is the one later used, that validation cannot be bypassed through an alternate route, and that canonicalization occurs before authorization decisions.
Verify authorization at the operation
For every read, write, delete, export, administrative action, and background job, identify the subject, resource, action, and policy decision. Check ownership and tenant boundaries on the server. A hidden button or a client-side role check is not authorization. Test both a permitted request and requests using another user’s identifier, a missing role, an expired session, and a direct URL or API call.
Inspect queries, errors, and limits
Look for concatenated SQL, NoSQL, LDAP, template, or search expressions; unsafe deserialization; unbounded pagination; expensive regular expressions; and loops controlled by attacker input. Check rate limits and account-lockout behavior where abuse is plausible. Confirm that failures are consistent, observable to operators, and appropriately vague to external callers.
Review cryptography and secret handling
Generated code may select deprecated algorithms, invent key-management logic, or place credentials in configuration that is shipped to clients. Verify the project’s approved cryptographic APIs and storage mechanism. Ensure secrets are injected through the established secret-management path, excluded from logs and error reports, and replaced if they were exposed during generation or testing.
4. Examine dependencies, build paths, and deployment changes
AI suggestions can include a package with a similar name, an unsuitable version, or a transitive dependency that changes the attack surface. Confirm the exact package identity and provenance, the reason it is needed, its license implications, and the version constraints recorded in the lockfile.
- Compare direct and transitive dependency additions and removals.
- Run the project’s software-composition analysis and review known advisories for the selected versions.
- Inspect install, build, test, and post-install scripts for shell commands, downloads, credential access, or unexpected network activity.
- Review container base images, package repositories, CI actions, deployment manifests, and infrastructure permissions.
- Check that development-only tools are not shipped into production and that generated bundles do not contain source secrets or debugging endpoints.
Verify model-suggested versions against current vulnerability databases and the project’s support policy. Model knowledge can be stale; a plausible version number is not evidence that a release is safe or maintained.
5. Run automated security checks, then investigate their limits
Run static application security testing (SAST), software-composition analysis (SCA), and secret scanning in the pull-request workflow, with findings visible to reviewers. Resolve findings or record a specific triage decision with an owner and rationale.
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| Automation | Useful for | Cannot establish by itself |
|---|---|---|
| SAST | Detecting recurring code patterns such as injection risks, unsafe APIs, or suspicious data flows supported by its analyzers. | That business rules, tenant isolation, or authorization semantics are correct. |
| SCA | Identifying dependency versions, known advisories, license data, and transitive changes. | That a dependency is configured safely or that an unlisted vulnerability does not exist. |
| Secret scanning | Finding tokens, keys, passwords, and credential-like material in tracked content and history where supported. | That no secret is exposed through logs, runtime configuration, screenshots, or an unrecognized format. |
A clean scan starts the review; it does not finish it. Scanners can miss broken access control, insecure workflows, race conditions, abuse cases, and business-logic flaws. Review false positives and false negatives as part of normal triage, and keep tool versions and rule sets recorded so results are reproducible.
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6. Review AI-written tests independently
Tests generated alongside implementation often mirror the implementation’s assumptions. Read them as evidence, not proof. A test that asserts a returned value may never exercise an unauthorized caller, malformed input, a timeout, or a partial failure.
Check security properties
- Unauthenticated, under-privileged, cross-tenant, and resource-owner scenarios.
- Invalid types, boundary lengths, duplicate requests, replayed tokens, and malformed encodings.
- Failure behavior for dependency outages, database errors, timeouts, and interrupted transactions.
- Logging and response behavior that must not disclose secrets or private data.
- Rate-limit, lockout, and recovery behavior where the endpoint can be abused.
For critical authentication, authorization, input-validation, and cryptographic logic, independently derive expected threats and write or request tests that express those properties. Passing tests prove only that the tested cases passed.
7. Audit the AI agent’s permissions and context
Review the development setup as part of the code review. Determine which files the assistant could read, which instructions and external content it received, and which tools, credentials, network destinations, and write operations were available.
- Grant the minimum repository, filesystem, network, and credential access needed for the task.
- Exclude production secrets, unrelated repositories, personal data, and sensitive files from model context where possible.
- Require explicit approval before high-impact writes, deployments, permission changes, migrations, or external uploads.
- Keep an audit trail of prompts, tool actions, generated patches, and human approvals when the project’s risk warrants it.
- Revoke temporary credentials and inspect outputs after an agent has handled untrusted content.
NIST SP 800-218A, the 2024 SSDF Community Profile for generative AI and dual-use foundation models, is a useful development-process reference. It does not replace the project’s threat model or a code review.
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For web content, identify the required WCAG 2.2 conformance level—A, AA, or AAA—and any product, contractual, or jurisdiction-specific requirements. WCAG 2.2 organizes testable success criteria under four principles: perceivable, operable, understandable, and robust. A project may need additional standards or legal analysis; do not treat a WCAG level as a universal legal conclusion.
Record the page types and states that must be evaluated: first load, empty and populated data, validation errors, authentication states, responsive layouts, dialogs, menus, loading states, and failure recovery. Generated components frequently work in the happy path while failing in a state that was not included in the prompt.
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9. Combine automated accessibility checks with manual review
Use an accessibility checker to find detectable patterns, then inspect meaning and interaction yourself. W3C’s Web Accessibility Initiative states: “Web accessibility evaluation tools can not determine accessibility, they can only assist in doing so.” Tools can produce false or misleading results, and they cannot check every aspect of conformance.
Document structure and headings
- Confirm one clear page or region purpose and a logical heading hierarchy.
- Use semantic landmarks and native controls where they express the interaction.
- Check that headings, labels, table headers, and status messages convey meaning rather than merely visual styling.
- Inspect dynamic updates to ensure important changes are announced appropriately without forcing a user to search the page.
Keyboard operation and focus
- Operate every control with a keyboard, including menus, dialogs, custom widgets, drag alternatives, and dismissal actions.
- Check a visible, sufficiently distinct focus indicator and a logical focus order.
- When a dialog opens, move focus into it, keep focus from escaping incorrectly, and return focus sensibly when it closes.
- Ensure no component creates a keyboard trap and that hover-only actions have an equivalent keyboard path.
Text alternatives, contrast, and presentation
Give informative images meaningful alternatives, mark decorative images appropriately, and provide equivalent text for icons whose purpose is not obvious. Check text and non-text contrast, zoom and reflow behavior, color-independent instructions, and readability at the project’s target viewport sizes. Do not infer accessibility from a screenshot alone.
Forms and errors
Associate every input with a visible, programmatically determinable label. Preserve entered data when validation fails where appropriate, identify the invalid field, explain how to fix it, and expose the error to assistive technology. Check required fields, format examples, autocomplete behavior, and success or failure confirmation for asynchronous submissions.
Motion, timing, and media
Inspect moving, blinking, auto-updating, or time-limited content and provide controls or alternatives where required. Check captions, transcripts, audio descriptions, pause controls, and non-motion alternatives for multimedia according to the target criteria.
10. Choose evaluation tools by capability, not brand
There is no universally best security scanner or accessibility evaluator. Select tools that fit the product and the team’s skills, and document what remains a human responsibility.
| Selection dimension | Questions to ask for accessibility evaluation | Questions to ask for security automation |
|---|---|---|
| Coverage | Which content types, WCAG versions, success criteria, browsers, and assistive-technology patterns are supported? | Which languages, frameworks, dependency ecosystems, and vulnerability classes are analyzed? |
| Runtime support | Can it test authenticated areas, JavaScript-rendered pages, multiple states, and component libraries? | Can it analyze generated code, infrastructure, containers, APIs, and runtime configuration? |
| Workflow | Is there a browser, command-line, component, or CI workflow that matches the team’s process? | Does it integrate with pull requests and builds without hiding findings from reviewers? |
| Reporting | Does each finding explain the affected element, criterion, evidence, and remediation limits? | Does each finding show data flow, affected dependency, severity rationale, and reproducible evidence? |
| Triage | Can reviewers mark false positives, assign owners, track fixes, and preserve audit history? | Can teams suppress narrowly, review exceptions, and manage false positives without disabling whole rules? |
| Cost and fit | Does the license and scope suit the number of pages, sites, users, and required evaluations? | Does the cost match repositories, contributors, scan frequency, and retention needs? |
W3C notes that accessibility-tool scope, supported products, features, and price vary. Compare the actual content and workflow you need rather than selecting a tool because it reports a large number of rules.
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11. Include people and usability evaluation when the flow warrants it
Conformance checks and real usability are related but not identical. Evaluate early and throughout development, not only immediately before release. For important or complex flows—such as account recovery, checkout, scheduling, data entry, or security settings—involve people with disabilities or experienced accessibility evaluators when feasible.
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Observe whether users can discover controls, understand status and errors, recover from mistakes, and complete tasks with their normal combination of keyboard, screen reader, magnification, voice input, or other technology. A page can satisfy individual success criteria and still impose confusing navigation or an unusable sequence.
12. Record a merge decision with a human owner
Finish with an auditable decision rather than a thumbs-up in chat. The record should identify:
- The change scope, intended behavior, affected data, and risk triggers.
- The reviewer, security or accessibility standard, target WCAG level where applicable, and project-specific requirements.
- Automated tools, versions, rule sets, scan dates, findings, and disposition of each exception.
- Manual security traces, accessibility checks, tested states, assistive-technology or usability evaluation, and unresolved limitations.
- The human maintainer and approver, plus follow-up tickets with owners and due dates.
Approval means the named owner accepts responsibility for correctness, security, accessibility work that remains, and future maintenance. If nobody is willing to own the generated code, it is not ready to merge.
A reusable pre-merge checklist
- Can I explain every changed file and its relationship to the requested behavior?
- Did I inspect dependencies, lockfiles, scripts, CI, containers, deployment files, and permissions?
- Does the change touch authentication, authorization, cryptography, parsing, deserialization, uploads, integrations, data stores, CORS, or public network exposure?
- Have I traced untrusted input to every security-sensitive use and verified server-side authorization at each protected operation?
- Did SAST, SCA, and secret scanning run, and are every finding resolved or explicitly triaged?
- Do tests cover hostile input, unauthorized users, cross-tenant access, failure paths, and security properties rather than only implementation details?
- Could the agent read or send sensitive context, use excessive credentials, or be steered by untrusted content?
- For web content, is the WCAG 2.2 target known, and have headings, labels, alternatives, contrast, keyboard access, focus, dynamic updates, and errors been checked?
- What did automation fail to evaluate, and who performed the manual or usability review?
- Is a human maintainer and approver recorded for the change?
Common review failures and how to recover
“The scanner is clean, so merge it.”
Reopen the review for authorization, business logic, abuse cases, and data boundaries. Add tests that express the missing security properties and document why the automated result is insufficient.
“The generated tests pass, so the feature is safe.”
Derive independent negative cases: another user’s resource, missing privileges, malformed input, replay, timeout, partial failure, and secret leakage. Have a reviewer who did not generate the tests inspect the assertions.
“The accessibility score is high.”
Test the actual keyboard path, visible focus, headings, labels, errors, dynamic announcements, zoom or reflow, and representative states. Ask an experienced evaluator or users with disabilities to try complex flows.
“It only changed a configuration file.”
Inspect permissions, network destinations, scripts, environment variables, build artifacts, and deployment effects. Configuration can widen trust boundaries without adding much application code.
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Bottom line
AI-generated code is acceptable when it has been treated like unfamiliar code, reviewed in proportion to its risk, checked by automation and humans, and assigned to a responsible maintainer. For web interfaces, use WCAG 2.2 to define the target, but never mistake an automated accessibility result for proof that people can use the product. The merge decision belongs to a human who understands the change and is prepared to maintain it.
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