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Test an AI-generated app as production software, not as a trustworthy answer from the generator. Define expected behavior and risk first, then combine independent functional tests, code and dependency review, adversarial runtime testing, web scanning where appropriate, and accessibility evaluation by both tools and people. A clean test run proves only that the tested cases passed; a human must review the evidence, approve the changes, and own the result.
Why AI-generated apps require independent testing
An AI coding system can produce a working-looking interface while misunderstanding a requirement, omitting an authorization check, mishandling an error, or introducing an inaccessible interaction. Its own tests are not independent assurance: an agent may delete a failing test, weaken an assertion, replace a real integration with a mock, or write a test that merely confirms the behavior it generated. OWASP’s Secure Coding with AI guidance recommends reviewing test changes as carefully as application changes and adding cases the agent did not author.
Use the generated code as an implementation to verify against an external specification. Your test plan should cover four questions:
- Does the app perform the required user-visible behavior, including failure paths?
- Can an untrusted user cross a trust boundary, expose data, or consume resources improperly?
- Does the implementation remain safe when inputs, dependencies, and runtime conditions are abnormal?
- Can people with different disabilities complete the important tasks with the stated accessibility target?
Assign a human owner to every AI-assisted change. That person reviews the diff, test evidence, security-sensitive code, and unresolved risk, then approves or rejects the release. Accountability cannot be delegated to the generator or to a scanner.
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Define expected behavior and risk before running tools
Turn requirements into acceptance criteria
Start with a short behavior inventory. For each important feature, record the actor, preconditions, inputs, successful result, visible errors, data written or exposed, and permissions required. Include states that are easy for a generator to miss: first use, empty data, duplicate submission, expired session, interrupted network request, and recovery after failure.
| Item to record | Questions to answer | Useful evidence |
|---|---|---|
| Behavior | What must the user be able to do, and what result confirms success? | Acceptance criterion and a reproducible example |
| Data | What data enters, leaves, or persists, and who may see it? | Field list, data-flow note, and sample records |
| Trust boundary | Where does control move between browser, API, worker, database, storage, or third-party service? | Simple architecture or data-flow diagram |
| Failure impact | What happens if this feature is wrong, unavailable, or abused? | Impact statement and priority |
| Accessibility need | Which complete task must be operable by keyboard and assistive technology? | Journey definition and target WCAG level |
NIST IR 8397 identifies threat modeling as a way to find design-level security problems before they become implementation defects. Use it to identify assets, entry points, trust boundaries, abuse cases, and the controls that should prevent or detect each abuse.
Choose a risk-based test depth
Test every feature, but spend the deepest review effort on authentication, authorization, payment or other high-consequence actions, file uploads, sensitive data, administrative functions, cryptography, and code that interprets untrusted content. A marketing page may need basic functional and accessibility checks; an account or data-management workflow needs source review, negative cases, dependency analysis, and runtime attack testing.
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Run the existing suite, then inspect what changed
Run unit, integration, end-to-end, and build checks that already exist. Do not treat a green result as sufficient until you inspect the application diff and the test diff together. Confirm that assertions still express the intended outcome, that tests reach the changed code, and that integrations that matter are exercised against realistic boundaries rather than being replaced by permissive mocks.
Look specifically for patterns OWASP warns about:
- A failing test removed instead of fixing the defect.
- An assertion changed from an exact security or business condition to a broad “no error” check.
- A real database, identity provider, queue, or authorization layer replaced by a mock that cannot expose integration failures.
- A test that copies the generated implementation’s assumption and therefore validates buggy behavior.
Add normal, negative, boundary, and historical cases
For each acceptance criterion, create at least one successful case and cases for malformed, missing, unexpected, and out-of-range input. Exercise minimum and maximum lengths, empty collections, unusual Unicode, duplicate requests, stale records, time-zone or date-boundary behavior, and network or service failures where those conditions are possible. Re-run defects that have previously occurred in the project; NIST includes historical bug tests among useful verification techniques.
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| Case family | Examples | What a failure may reveal |
|---|---|---|
| Normal use | Valid sign-in, create, edit, search, and delete flows | Missing or incorrect core behavior |
| Missing input | Omitted required field, empty upload, absent session token | Null handling, accidental defaults, or bypassed validation |
| Malformed input | Invalid encoding, wrong type, oversized value, unexpected structure | Parser errors, injection paths, or unsafe error handling |
| Boundaries | Length limits, numeric extremes, first and last date, empty and very large result sets | Off-by-one defects, overflow, pagination, and performance problems |
| Repeated or concurrent action | Double-click submit, replayed request, two edits at once | Duplicate charges, race conditions, and lost updates |
| Recovery | Refresh during save, expired credential, dependency timeout, partial failure | Data corruption, misleading status, or unrecoverable state |
Confirm the test actually checks the result
Read assertions for semantic strength. A test should verify the returned status, authorization outcome, persisted data, side effects, and user-visible state that matter for that case. Snapshot tests and broad “page loaded” checks can be useful, but they do not replace assertions about permissions, validation, error messages, or durable state. Preserve test output, environment details, and the commit identifier so a failure can be reproduced.
Inspect code, secrets, and dependencies
Review security-sensitive implementation directly
Read changes involving authentication, authorization, input validation, file handling, data access, session management, redirects, cryptographic operations, deserialization, and error logging. Check that authorization is enforced on the server or service boundary, not only by hiding a client-side control. Verify that the code fails closed, validates server-side, limits resource use, and does not disclose credentials or sensitive records in errors, logs, or responses.
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Use static analysis and secret checks
Run static analysis appropriate to the language and framework, and review the findings rather than blindly suppressing them. Use heuristic secret checks for hardcoded keys, tokens, passwords, private certificates, and connection strings. Confirm that test fixtures do not contain production credentials and that secrets are supplied through the deployment’s approved secret mechanism. NIST IR 8397 lists static scanning, heuristic secret checks, and built-in protections as baseline techniques, while noting that they are not the totality of verification.
Examine included code and services
Inventory direct and transitive packages, generated bundles, container images, plugins, hosted services, and runtime versions. For each material dependency, record its version, purpose, update source, and whether it handles sensitive data. Check advisories and license or support constraints according to your project’s policy. Test the application’s use of the dependency; a package may be well maintained while the app still calls it unsafely.
| Review layer | Questions | Evidence to keep |
|---|---|---|
| Source | Are validation, authorization, error handling, and cryptography implemented as intended? | Reviewed diff and notes on security-critical paths |
| Secrets | Could credentials appear in source, history, logs, artifacts, or test data? | Scanner result, remediation record, and rotation confirmation if needed |
| Dependencies | Which libraries, packages, services, and generated assets are included? | Dependency inventory and update decisions |
| Configuration | Are debug settings, default accounts, CORS, cookies, headers, and permissions safe for deployment? | Environment-specific configuration review |
Attack the running application
Exercise external behavior and negative paths
Use black-box tests against the deployed or test instance. Attempt invalid and expired credentials, access to another user’s identifier, direct requests to administrative endpoints, malformed JSON or form data, unexpected content types, oversized uploads, and repeated actions. Check both the response and the resulting state: an endpoint that returns an error but still writes data is a defect.
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Where the application parses complex or attacker-controlled input, fuzz appropriate surfaces with bounded inputs and an isolated environment. Fuzzing is useful for finding crashes, parser inconsistencies, and unexpected states; it does not establish that business authorization is correct.
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Run a web application scanner when the app is reachable from the internet or another untrusted network. Treat each result as evidence about the scanner’s checks. Reproduce it, determine exploitability and impact, locate the responsible code or configuration, and record false positives with an explanation. NIST recommends black-box test cases, fuzzing, web application scanning where applicable, and fixing critical bugs as parts of a broader verification program.
Never place production secrets or uncontrolled destructive actions in a scanner’s target environment. Use representative data, rate limits, backups, and a defined test window.
Test identity, authorization, and state transitions
- Verify that an unauthenticated request cannot reach protected data or actions.
- Verify that an authenticated user cannot read or modify another user’s records by changing an identifier.
- Verify role changes, revoked sessions, expired tokens, and password or credential resets.
- Verify that a workflow cannot skip required states, such as approval before publication or payment before fulfillment.
- Verify that repeated requests are idempotent where they should be and rejected or safely handled where they should not be repeated.
Test accessibility against a stated WCAG 2.2 target
Select the conformance level and scope
State whether the goal is WCAG 2.2 Level A, AA, or AAA, and identify the pages, routes, dialogs, emails, and embedded components within scope. WCAG 2.2 is a W3C Recommendation published October 5, 2023. Conformance applies to complete pages and complete processes, not a single attractive widget or a screenshot.
| Target | How to use it in a test plan |
|---|---|
| Level A | Establish the baseline success criteria that apply to the product and its essential content. |
| Level AA | Add the applicable AA criteria and make them release requirements for the stated scope. |
| Level AAA | Apply only where the organization has deliberately selected the applicable AAA criteria; it is not a blanket guarantee for every type of content. |
Test complete user journeys
Choose important end-to-end processes such as account creation, sign-in, form submission, search, file upload, purchase, or support contact. Include every page and state needed to finish the task: validation errors, loading indicators, confirmation, modal dialogs, empty results, permission errors, and recovery after failure. A compliant-looking component cannot compensate for an inaccessible step later in the process.
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Combine automation with human evaluation
Run an accessibility evaluation tool to identify issues it can detect, such as some missing names, labels, structural relationships, or contrast failures. W3C WAI explains that evaluation tools cannot check every accessibility aspect automatically; human judgment is required. Do not describe a scan as proof of WCAG compliance.
Manually evaluate context-sensitive behavior:
- Whether alternative text communicates the purpose of an image rather than merely its file name.
- Whether headings, labels, instructions, and error messages make the task understandable.
- Whether focus moves predictably, remains visible, and is returned sensibly after a dialog closes.
- Whether every control and custom widget works with a keyboard without a pointer.
- Whether dynamic updates, validation errors, status messages, and expanded or collapsed states are exposed to assistive technology.
- Whether zoom, reflow, text spacing, motion, timing, and orientation create barriers on the supported devices.
Use keyboard-only testing and assistive technology checks for core journeys. When feasible, add usability sessions with people with disabilities; W3C distinguishes usability testing from functional conformance testing and recommends using both for a stronger evaluation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose tools and review depth by evidence, not brand names
Candidate tools should be compared against the app and the evidence you need, rather than selected from a generic ranking. For security and functional testing, assess:
| Decision axis | Questions to ask |
|---|---|
| Technology coverage | Does it understand the language, framework, runtime, build system, and deployment model? |
| Inspection surface | Does it inspect source, dependencies, secrets, configuration, a running app, or only one of these? |
| CI integration | Can it run at the right stage without exposing sensitive data or blocking unrelated work? |
| Evidence quality | Does each finding include location, reproduction details, severity rationale, and a stable result that can be compared over time? |
| Data constraints | Can the tool operate within your privacy, network, retention, and deployment requirements? |
For accessibility tools, also ask which WCAG 2.2 criteria they test, which page states they can reach, how they expose manual review tasks, and how reviewers will cover full processes. A tool that checks only the initial route cannot establish accessibility of a multi-step workflow.
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When the product itself contains AI
Do not confuse an app generated with AI assistance with an app that exposes an AI model to users. If the product includes a model, agent, retrieval system, or AI-dependent workflow, consider OWASP’s AI Security Verification Standard (AISVS) in addition to ordinary application security. AISVS 1.0 was released in June 2026 and contains 191 requirements across 12 chapters and three appendices; each requirement has a verification level of 1, 2, or 3.
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Use the standard when AI behavior is part of the product’s attack surface or safety case. Add tests for the model-integrated boundaries that apply to your design, such as untrusted instructions entering prompts, retrieval data crossing tenant boundaries, tool calls changing records, output being rendered as code or markup, and sensitive information appearing in responses. Keep the ordinary authorization, input, dependency, and accessibility checks: an AI-specific catalogue supplements them rather than replacing them.
Triage findings, fix them, and retest
Record reproducible issues
For each finding, record the affected requirement or behavior, environment and build identifier, exact reproduction path, observed and expected results, evidence such as logs or screenshots, impact, severity, owner, and status. A useful report lets another person reproduce the issue without asking the generator what it intended.
Use a release decision based on impact
| Disposition | Release treatment |
|---|---|
| Critical security or data-integrity defect | Block release, contain exposure, fix, and rerun the relevant attack and regression cases. |
| High-impact functional or accessibility barrier on a core journey | Block the affected release path until corrected or explicitly accepted by the accountable owner with a documented mitigation. |
| Moderate defect with a reliable workaround | Assign an owner and deadline, verify the workaround, and track it through the next release. |
| Low-impact issue or confirmed false positive | Document the rationale and retain the evidence so the decision is auditable. |
NIST recommends fixing critical bugs and retaining historical tests. After a fix, rerun the failed test, neighboring cases, and the relevant regression suite. Recheck security findings against the running app and repeat accessibility evaluation for the affected page and complete process. Preserve the test history, tool versions, configuration, and approval record so a later change can reveal a regression.
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A repeatable release sequence
- Write acceptance criteria, data and permission expectations, trust boundaries, accessibility scope, and the chosen WCAG 2.2 level.
- Run existing automated checks and inspect both application and test diffs for weakened or self-confirming tests.
- Add normal, malformed, missing, boundary, repeated, recovery, and historical defect cases.
- Review security-critical code, configuration, secrets, dependencies, generated assets, and services.
- Exercise the running app with black-box negative tests, authorization checks, fuzzing where suitable, and a web scanner where applicable.
- Evaluate complete accessibility journeys with automation, keyboard operation, assistive technology, and human judgment.
- Log, reproduce, prioritize, fix, and retest findings; block critical issues.
- Have a named human owner review the evidence and approve the release or document the accepted residual risk.
This sequence gives you layered evidence rather than a misleading single score. NIST’s minimum techniques are broadly applicable starting points, not a claim that every possible verification method has been performed. The appropriate depth remains dependent on the app’s architecture, users, data sensitivity, and consequences of failure.
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