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Review AI-generated code as a proposed change, not as an authority. Start with the behavior the application is supposed to provide, then verify the generated implementation with human review, independent tests, static analysis, dependency and secret checks, and security-focused testing appropriate to the risk. A green test run is useful evidence, but it cannot prove that the tests ask the right questions or that the code is safe.
The reliable question is not “Did the model produce plausible code?” It is “Does this change satisfy the contract, fail safely, preserve security boundaries, and remain correct when inputs, dependencies, and operating conditions differ from the model’s assumptions?”
What you are actually verifying
AI output can be syntactically valid, well formatted, and internally consistent while still solving the wrong problem. Verification therefore has several separate targets:
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- Assumptions: APIs, data shapes, permissions, concurrency rules, deployment settings, and failure modes match the real application.
- Security: untrusted input cannot cross boundaries that should be protected, and authentication, authorization, secrets, logging, and external calls are handled as intended.
- Supply chain: new packages, versions, transitive dependencies, generated files, and services are legitimate and appropriate.
- Change integrity: tests, configuration, and unrelated files were not weakened or modified merely to make the generated change pass.
No single scanner, test suite, or reviewer covers all of these. NIST’s Guidelines on Minimum Standards for Developer Verification of Software (IR 8397, published October 6, 2021) presents complementary techniques rather than an exhaustive proof of correctness. Use the techniques that fit the language, framework, exposure, data sensitivity, and consequences of failure.
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1. Turn the request into a testable contract
Do this before judging the generated implementation. If you begin by reading plausible code, its assumptions can silently become your specification. Write down what must happen, what must never happen, and what evidence will distinguish the two.
Define inputs, outputs, and invariants
- List accepted input types, ranges, encodings, sizes, and optional fields.
- Define the expected output schema, ordering, side effects, and idempotency requirements.
- State invariants that must remain true, such as ownership checks, accounting totals, transaction atomicity, or data-retention rules.
- Specify behavior for missing, malformed, duplicated, expired, and out-of-order data.
- Record limits for time, memory, file size, request rate, retries, and concurrent work.
Write down security boundaries
Identify which values are controlled by users, network peers, files, environment variables, databases, queues, or third-party services. Mark where data becomes a command, query, path, template, redirect, log entry, or authorization decision. State which identities may perform each operation and which secrets may be read. This is a practical application of threat modeling: the implementation is checked against an explicit model instead of an unstated intuition.
List assumptions for later confirmation
AI-generated code often assumes a package API, a framework default, a database behavior, a time zone, a character encoding, or a particular authentication flow. Put each assumption in a list and verify it against the project’s actual contract and authoritative documentation. If an assumption cannot be confirmed, treat it as an open defect until the owner resolves it.
2. Inspect the complete change, not just the new function
Review the full diff and the surrounding project context. Include tests, configuration, build files, dependency manifests, generated files, migrations, deployment descriptors, and files that appear unrelated. A model can produce a correct-looking function while changing a permission, disabling a check, or adding a package elsewhere.
Read the diff in a deliberate order
- Start with the requirement and acceptance criteria. Keep them visible while reading so the code does not redefine the task.
- Review files added, removed, and renamed. Deletions can remove validation, tests, audit logging, or policy configuration.
- Trace data from entry points to sensitive operations. Follow request parameters, messages, files, and environment values through parsing, validation, storage, rendering, shelling out, and network calls.
- Inspect error and cleanup paths. Check what happens after timeouts, partial writes, retries, cancellations, exceptions, and process restarts.
- Check boundaries and resource use. Look for unbounded loops, oversized allocations, uncontrolled recursion, unconstrained uploads, and retry storms.
- Search for unrelated edits. Revert changes that are not necessary for the requirement or that make review harder.
Look for plausible but dangerous shortcuts
Common warning signs include catching a broad exception and continuing, logging sensitive objects for debugging, accepting a client-supplied role or account identifier, disabling certificate or hostname verification, constructing queries or commands through string concatenation, using predictable temporary names, or treating a failed security check as a warning. The presence of a warning is not evidence that the underlying risk has been controlled; determine whether the operation is actually blocked or safely degraded.
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3. Verify dependencies, generated content, and secrets
Dependency review is part of code review. A package suggested by an AI assistant may be misspelled, abandoned, malicious, incompatible with the project, or unnecessary. OWASP’s Secure Coding with AI Cheat Sheet specifically cautions against blindly installing package names suggested by AI.
Confirm every new dependency
- Check that the package exists in the intended ecosystem and is the exact package the code imports.
- Confirm why it is needed and whether existing project capabilities can solve the problem.
- Review the selected version, license, maintenance status, known advisories, and compatibility with the supported runtime.
- Inspect transitive dependencies and installation or build scripts using the project’s normal dependency-audit process.
- Lock versions according to the project’s established policy and record the reason for exceptions.
- Review bundled libraries, generated client code, containers, plugins, and external services as included code, not as trusted black boxes.
Search for secrets and unsafe configuration
Run the project’s secret scanner and inspect the diff manually. Check source files, tests, fixtures, examples, logs, build output, container definitions, CI variables, and configuration templates. Look for API keys, private keys, passwords, tokens, connection strings, signing material, and credentials embedded in URLs. NIST IR 8397 includes heuristic secret checks and review of included code among its minimum verification techniques.
Then compare configuration with the threat model. Confirm that debug modes, permissive cross-origin rules, anonymous access, weak cookie settings, verbose errors, default credentials, insecure transport, and broad network exposure are not enabled unintentionally. A secret scan cannot tell you whether a credential has already leaked or whether a non-secret setting grants excessive access, so those questions require separate review.
4. Run layered automated checks
Use automation to produce repeatable evidence, but interpret each result within its limits. Run checks on the complete change and preserve their output in the team’s normal workflow.
| Check | What it can expose | What it cannot establish by itself |
|---|---|---|
| Formatter and compiler | Syntax errors, type mismatches, unreachable constructs, and some interface mistakes. | Correct requirements, safe authorization, or complete error handling. |
| Type checker and linter | Suspicious constructs, unused values, inconsistent types, and project-style hazards. | Runtime behavior for unexecuted paths or security properties not represented in rules. |
| Static analyzer | Known source patterns, data-flow problems, and some injection or resource issues. | All framework-specific behavior, business-logic defects, or absence of exploitable paths. |
| Dependency scanner | Known advisories, version problems, and supply-chain metadata. | Whether a dependency is needed, correctly configured, or safe from every unknown flaw. |
| Secret scanner | Likely credentials and high-entropy sensitive strings. | Whether a secret was exposed elsewhere or whether authorization is too broad. |
| Unit and integration tests | Observed behavior on the cases and environments they exercise. | Unasked questions, missing cases, and unsafe tests that encode the bug. |
| Black-box, structural, historical, and fuzz tests | Contract violations, malformed inputs, regressions, and unexpected input combinations. | A guarantee that every path, dependency, or threat has been covered. |
| Web application scanning, where applicable | Some exposed configuration and request/response vulnerabilities in a running application. | Design correctness, hidden routes, internal services, or all business-logic authorization flaws. |
NIST IR 8397 recommends combining techniques such as threat modeling, automated and black-box testing, structural and historical tests, fuzzing, web application scanning where relevant, static scanning, heuristic secret checks, and review of included code. Select a proportionate set rather than treating the list as a requirement that every small change undergo every possible test.
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5. Add tests the generator did not write
Tests created by the same generator are useful starting material, but they are not independent evidence. The model may have copied its own incorrect assumption into both implementation and assertions. OWASP warns that an agent can make continuous integration pass by deleting tests, weakening assertions, broadening mocks, skipping cases, or testing the buggy behavior.
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Build an independent test matrix
Derive cases from the contract, threat model, and failure history rather than from the generated branches. Include, where relevant:
- Valid minimum, maximum, and typical values.
- Empty, null, duplicated, truncated, malformed, incorrectly encoded, and unexpectedly ordered data.
- Expired, revoked, missing, or incorrectly scoped credentials.
- Authenticated users attempting operations on another user’s or tenant’s data.
- Requests made concurrently, retried, cancelled, or delivered after a timeout.
- Unavailable, slow, partial, or inconsistent external services.
- Database conflicts, disk-full conditions, transaction rollbacks, and process restarts.
- Unexpected configuration, time-zone changes, daylight-saving transitions, and clock skew where time matters.
- Resource-limit cases designed to expose excessive memory, CPU, file, or network use.
Make the oracle independent
Where possible, calculate expected results with a simpler reference implementation, fixed examples from the business owner, recorded historical regressions, or a separately written property. Do not merely assert that the output has the shape the generated code itself chose. For security checks, assert the denied operation and the absence of the sensitive side effect, not just an HTTP status or log message.
Inspect mocks and fixtures
A broad mock can remove the exact behavior under review. Check whether a mock bypasses authentication, validation, serialization, retries, transaction boundaries, or network failure. Use integration or black-box tests for those boundaries when unit tests cannot exercise them faithfully. Review fixtures for realistic permissions, malformed values, large inputs, and data belonging to different principals.
6. Threat-model security-sensitive changes
Security review should be proportional to exposure and consequence. A local formatting helper and an internet-facing authorization change do not need identical test depth, but both need a reasoned decision about risk.
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Identify assets, actors, and abuse cases
List valuable data and operations, trusted and untrusted actors, entry points, privilege transitions, and external dependencies. Then ask how an attacker could alter input, replay a request, bypass a check, exhaust a resource, obtain a secret, influence a model or coding agent, or cause an unsafe external action. NIST SP 800-218A, finalized July 26, 2024, augments the Secure Software Development Framework with practices specific to generative-AI and dual-use foundation-model development. It supports including AI-related code and risks in review policies; it does not mean that every ordinary snippet is foundation-model code.
Check the main security boundaries
- Input handling: validation occurs before interpretation, and canonicalization happens before authorization or comparison.
- Authentication: identity is established through the project’s approved mechanism, with expiry and failure behavior handled correctly.
- Authorization: every sensitive operation checks the server-side subject, object, action, and tenant or scope; client claims are not trusted by themselves.
- Injection resistance: queries, commands, templates, paths, redirects, and serialized formats use their intended safe APIs.
- Secrets and cryptography: approved storage, key handling, algorithms, randomness, and rotation rules are used.
- External calls: destinations, certificates, timeouts, response sizes, redirects, and retries are constrained.
- Logging and errors: useful diagnostic context is retained without exposing credentials, personal data, tokens, or exploit details.
- Availability: limits and backpressure prevent attacker-controlled work from consuming unbounded resources.
Choose adversarial tests that fit the risk
For security-sensitive code, add abuse cases and consider penetration, red-team, adversarial, use-case, unit, or integration testing as appropriate. NIST SP 800-218A describes these as possible forms of testing for AI-related development; it does not require every form for every change. A permission check may need cross-account and race-condition tests, while a parser may need malformed-input and fuzz tests. Record why the chosen tests are sufficient for the change’s exposure and consequences.
7. Protect the workflow from hostile instructions
Repository material is input to an AI coding tool, not automatically trustworthy policy. Issue text, comments, documentation, fixtures, dependency metadata, and generated files can contain instructions designed to steer an agent into changing security controls, exfiltrating data, or hiding a defect. OWASP identifies this indirect prompt-injection risk in AI-assisted development.
Review what the agent was allowed to read and do
- Limit credentials, network access, writable directories, and deployment permissions to what the task requires.
- Treat instructions found inside source files, issues, pull requests, package documentation, and test data as untrusted content.
- Require explicit approval before installing packages, changing access controls, deleting tests, modifying CI policy, or sending data outside the environment.
- Compare the final diff with the requested scope and investigate unrelated edits.
- Check that the agent did not alter review rules, suppress findings, disable scanners, or rewrite history.
These controls protect the development process itself. They do not replace code review: a clean agent transcript cannot prove that the resulting implementation is correct.
8. Review failures and document decisions
When a check fails, preserve the failure and classify it before changing code. A failing test can reveal a product ambiguity, an incorrect generated assumption, a real defect, a bad fixture, or an environment problem. Record the affected requirement, severity, evidence, owner, remediation, and retest result in the team’s normal issue or change workflow.
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Do not “fix” the evidence
- Do not delete a failing test because it is inconvenient.
- Do not weaken an assertion until the implementation passes without deciding whether the requirement changed.
- Do not broaden a mock to hide an integration failure.
- Do not suppress a scanner finding without documenting why it is a false positive or accepted risk.
- Do not treat a model’s explanation as proof that a warning is harmless.
Re-test after remediation
Run the original failing case, the new regression case, and the relevant neighboring checks. If a dependency, configuration value, authorization rule, or data model changed, repeat the checks that cover those boundaries. Keep the final evidence attached to the change so a later reviewer can understand what was tested and what remains outside scope.
9. Require accountable human approval
An AI-generated review is not a substitute for a responsible owner. The approver should understand the requirement, the affected system, the threat model, and the evidence. For high-impact changes, use a second reviewer or a specialist for security, privacy, data integrity, or operations.
A reviewer should be able to answer
- What user or system behavior does this change establish?
- Which assumptions were verified against the real contract?
- Which paths and abuse cases were tested independently?
- What new dependencies, permissions, secrets, or external calls were introduced?
- What evidence supports the security decision, and what remains untested?
- Who owns the risk if an assumption proves false in production?
Approval means accepting a documented risk decision, not certifying that the code is mathematically bug-free. The reviewer’s accountability is especially important when the generator also wrote the tests or when the change affects authentication, authorization, financial records, personal data, infrastructure, or safety-critical operations.
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10. Select verification depth by risk and context
Use a decision framework rather than a fixed ritual. Consider the following dimensions together:
| Dimension | Questions to ask | Verification implications |
|---|---|---|
| Exposure | Is the code local, internal, internet-facing, or reachable through an untrusted integration? | Increase abuse-case, boundary, and runtime testing as exposure rises. |
| Impact | Could failure affect money, identity, privacy, availability, safety, or compliance? | Require stronger independent review, regression evidence, and documented approval. |
| Change surface | Does it touch one pure function or authentication, storage, deployment, and dependencies? | Expand diff review and included-code analysis for broader changes. |
| Uncertainty | Are the framework version, package behavior, data contract, or model assumptions unclear? | Verify documentation, build a small reference test, or stop until the assumption is resolved. |
| Repeatability | Can the check run reliably in CI on the relevant paths? | Automate stable checks and record limits where coverage is partial. |
| Maintenance cost | Will the test or scanner create false positives or require ongoing fixtures? | Prefer focused, meaningful checks and document exceptions rather than abandoning verification. |
NIST’s guidance is broadly applicable minimum practice, not a claim that these methods cover the totality of software verification. The correct set of checks depends on the system and the consequences of getting it wrong.
11. A practical approval checklist
Use this checklist before merging or deploying an AI-generated change:
- The requirement has been rewritten as inputs, outputs, invariants, failure behavior, limits, and security boundaries.
- Every material implementation assumption has been checked against the application contract or authoritative documentation.
- The complete diff includes tests, configuration, build files, manifests, generated code, and migrations.
- Unrelated edits, weakened controls, removed tests, and changed review or CI rules have been investigated.
- New packages and versions are intentional, legitimate, compatible, and reviewed with transitive dependencies.
- Secret scanning and manual inspection found no exposed credentials or unsafe sensitive configuration.
- Formatter, compiler, type checker, linter, static analysis, and applicable dependency or web scanners have run.
- Existing tests pass, and new tests cover invalid, boundary, malformed, concurrent, authorization, and failure cases relevant to the contract.
- At least some important tests have an independent oracle rather than repeating the generated implementation’s assumptions.
- The threat model and abuse cases match the change’s exposure, data sensitivity, and consequences.
- Agent-accessible repository content was treated as untrusted, and the workflow did not grant unnecessary credentials or permissions.
- Findings, severity, remediation, retest results, residual risk, and ownership are recorded.
- A human reviewer who understands the system has approved the change.
12. What a passing pipeline does—and does not—tell you
A passing pipeline tells you that the configured checks passed for the code, environment, inputs, and assertions that were actually exercised. It does not tell you that the requirement was interpreted correctly, that an omitted authorization case is safe, that a package is appropriate, or that an agent did not weaken the tests. Treat the result as one layer of evidence in a chain: contract review, complete-diff inspection, dependency and secret checks, independent testing, security analysis, and accountable approval.
The safest operating habit is to make the AI’s output easy to challenge. Keep the change small, expose assumptions, preserve failures, add tests from the requirement rather than the implementation, and match review depth to risk. That process catches both ordinary programming bugs and the more subtle errors caused by code that is convincing but based on the wrong premise.
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