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Review AI-generated code as if it came from an unfamiliar contributor. First establish what the change is supposed to do, then trace data and control flow, inspect authorization and other security boundaries, verify every dependency, review the tests as code, run layered automated checks, and examine the AI tool’s permissions and context. A named human who understands the change must approve it. Passing tests or an AI-written explanation is useful evidence, but neither proves correctness or security.
Why AI authorship does not lower the review standard
Generated code can look consistent, compile cleanly and follow familiar patterns while encoding a wrong requirement. It may assume a user is authorized because a button is hidden, trust a value that has not been validated, choose an unsafe cryptographic mode, or install a package whose name was invented. OWASP’s Secure Coding with AI Cheat Sheet states that AI-generated code needs at least the scrutiny applied to code from an unknown contributor, with human review for security-relevant changes.
Do not approve a change that neither the author nor the reviewer can explain. A polished generated explanation or review comment is a lead for investigation, not proof that the behavior matches the product’s contract. The accountable approver should be able to describe the intended behavior, important assumptions, failure modes and operational consequences.
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Can you trust AI-generated code when the tests pass?
No. A passing suite is evidence only for the cases it actually exercises and the expected behavior encoded in those tests. A generator can produce tests that bless its own mistaken implementation, weaken assertions, replace real dependencies with mocks, delete difficult cases or omit hostile inputs. OWASP cautions against using pass rate alone as security confidence.
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Keep the passing result, but challenge its coverage and its oracle—the definition of what “correct” means. Add independently designed negative, boundary and adversarial cases. Review test changes with the same care as production changes, and require stronger evidence for authentication, authorization, deserialization and cryptographic behavior.
A risk-based review model
Review depth should follow consequence and exposure rather than the amount of generated text. Use these axes before deciding how much scrutiny a change needs:
| Risk question | Higher-risk signal | Review response |
|---|---|---|
| What happens if it is wrong? | Financial loss, data disclosure, safety impact or service-wide outage | Require a domain owner, detailed tests and explicit approval before merge |
| Does it cross a trust boundary? | Internet request, tenant boundary, privileged service, queue, file upload or external API | Trace untrusted data end to end and verify checks at the enforcement point |
| Which security decisions changed? | Authentication, authorization, cryptography, parsing, secrets, IAM or deployment controls | Use a security specialist or two-person review where policy requires it |
| How independent is the evidence? | Few tests, generated tests only, heavy mocking or no production-like exercise | Add independent tests, property-based or fuzz testing and runtime checks |
| What could the AI tool access? | Credentials, proprietary code, personal data, terminal and network access | Inspect the tool configuration, reduce permissions and record the context used |
Routine automated gates should run for every change. Sensitive code and infrastructure deserve deeper manual analysis and, under the OWASP AISVS 1.0 guidance for AI-generated code, a higher approval threshold such as two-person or security-team sign-off.
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Start with the issue, design note, API contract, data model and relevant existing behavior. Write down what should change and what must remain invariant. Then compare that statement with the actual diff.
Questions to answer
- Which files, public interfaces, schemas and workflows are expected to change?
- Does the diff add an endpoint, background job, data store, permission, dependency or deployment setting?
- Which trust boundaries, tenants, identities and data classifications are involved?
- What error behavior, latency limit, retry policy and state transition does the design require?
- Did the change modify agent instruction files, hooks, CI definitions or other files that can influence future generations?
Treat instruction files and hooks as security-relevant configuration. A malicious or careless rule can make later generated changes unsafe even when the current source diff appears harmless. Review those files with the same attention given to build and deployment configuration.
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Step 2: Trace data and control flow
Follow every untrusted value from its entry point to storage, computation and output. Mark where identity is established, where authorization is enforced, where validation occurs and where data leaves the system.
Boundary and authorization checks
- Authenticate the caller before relying on identity claims.
- Authorize the specific action and object at the service or data-access boundary, not only in a user interface or route wrapper.
- Check tenant, ownership and role constraints again after internal calls or asynchronous hand-offs.
- Ensure denied requests fail closed and do not reveal whether a protected object exists.
Input and output handling
- Validate type, length, encoding, range and allowed values before dangerous use.
- Reject malformed, duplicate, oversized and unexpectedly nested payloads.
- Use parameterized database operations rather than string-built queries.
- Avoid dynamic execution such as
evalwhen data can influence code or expressions. - Encode output for its actual context, such as HTML, a shell, a URL, JSON or a log record.
- Examine deserialization settings and allowlists; do not deserialize attacker-controlled types by default.
Failure, concurrency and resource behavior
Inspect the paths that generated code often treats as secondary: timeouts, retries, cancellation, partial writes, duplicate messages, concurrent updates and exhausted resources. Check whether retries can repeat a payment or other non-idempotent action, whether locks are released on exceptions, and whether an attacker can force unbounded memory, CPU, disk or network use. Verify state transitions rather than only the successful path.
Step 3: Inspect security-critical decisions and configuration
Do not accept a familiar API name as evidence of safe use. Verify the concrete algorithm, mode, key lifecycle, nonce or IV handling, randomness source, rotation plan and failure behavior. Confirm that secrets are not hard-coded, logged, committed to fixtures or exposed through error messages.
Configuration areas that deserve elevated scrutiny
- Authentication providers, session lifetime, token audience and issuer checks
- Authorization policy, role mapping, object-level access and administrative bypasses
- Cryptographic libraries, algorithms, modes, key storage, nonce reuse and downgrade behavior
- Serialization formats, parser limits, compression and archive extraction rules
- IAM policies, service accounts, cloud roles, network routes and firewall rules
- Container, sandbox and deployment manifests, including debug mode and public bindings
- CI/CD definitions, release approvals, artifact provenance and secret injection
When an AI change touches security-critical files, apply the AISVS 1.0 recommendation for a higher threshold, such as two reviewers or security-team sign-off. The exact gate should follow your organization’s policy, but the principle is consistent: sensitive configuration is not routine boilerplate.
Step 4: Verify every dependency before installing or accepting it
Generated code may name a package that does not exist, confuse similarly named packages, select an abandoned component or introduce a dependency with malicious code. An attacker can register a plausible name and wait for developers to run a generated install command.
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- Confirm the exact package name in the intended registry and verify that it provides the API the code calls.
- Check provenance, publisher identity, release history, maintenance activity and licensing against your project’s policy.
- Inspect the resolved version and transitive dependency tree rather than trusting a broad version range.
- Run software-composition analysis and vulnerability checks before merge and again in continuous integration.
- Prefer an internal registry proxy, allowlist or package-age control where your environment supports one.
- Review install and build scripts for network access, credential use and arbitrary command execution.
Never run a generated install command solely because it looks conventional. Treat package selection as a security decision, not a formatting detail.
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Read the test diff before trusting its green result. Look for deleted cases, assertions changed to loose truthiness checks, snapshots updated without inspection, and mocks that bypass the dependency or security boundary under test.
Cases to add independently
- Empty, malformed, oversized, duplicated and unexpected input
- Expired, revoked, malformed or wrong-audience credentials
- Cross-tenant and object-level access attempts
- Boundary values, time-zone transitions and integer or date overflow
- Concurrent requests, duplicate delivery and retry after partial failure
- Network timeouts, dependency errors, unavailable key services and corrupted state
- Parser and deserializer abuse, including deeply nested or hostile structures
For security-critical behavior, independently review tests for authentication, authorization, input validation and cryptographic operations. AISVS identifies differential fuzzing and property-based testing as useful for input validation, authorization logic and deserialization safety. These techniques explore behavior beyond the examples a generator selected.
Step 6: Run layered automated checks and interpret them correctly
Run the project’s normal tests and security gates, then interpret every result in context. A practical pull-request layer includes:
| Check | What it can reveal | What it cannot establish by itself |
|---|---|---|
| Static analysis (SAST) | Dangerous APIs, taint flows, injection patterns and some control-flow defects | Whether the business operation is authorized or the design matches product rules |
| Software composition analysis (SCA) | Known vulnerable or disallowed dependencies and license policy violations | Whether a newly chosen package is appropriate for the intended behavior |
| Secret scanning | Likely keys, tokens and credentials in source or history | Secrets hidden through encoding, runtime injection or external systems |
| Infrastructure-as-code scanning | Public exposure, excessive permissions and insecure deployment defaults | Whether the complete runtime architecture is safely configured |
| Dynamic or runtime testing | Observed behavior, integration failures and some exploitable paths | Unexercised states, rare races and requirements not represented in tests |
A pull request containing AI-generated code may reasonably use SAST, interactive or dynamic analysis, secret scanning, IaC scanning and SCA, as described by AISVS. Configure merge gates for critical findings according to your severity policy. A clean scan is the beginning of contextual review, not its end.
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Neutral tool choices
Teams can layer tools into their existing workflow rather than creating a separate “AI” pipeline. Examples named in OWASP guidance include Semgrep Community Edition, GitHub Code Security and CodeQL, and Snyk Code. Compare them by language and framework coverage, repository integration, alert quality, policy controls and total cost after checking current vendor details. GitHub describes CodeQL as a semantic analysis engine and its Autofix feature as a generator of proposed remediations for CodeQL alerts; either is an aid for finding or fixing findings, not proof that the complete change is correct.
Step 7: Examine the AI workflow, not just the resulting diff
Security risk can enter through what the assistant saw and what it was allowed to do. Review the prompt and session context when policy permits, including files, terminal output, credentials, personal data, proprietary algorithms and architecture details sent to a provider.
Controls for coding assistants and agents
- Exclude secrets, credential stores, personal-data directories and unrelated repositories from context where supported.
- Use least-privilege file, terminal and network permissions.
- Run agents in a sandbox or isolated workspace with controlled egress.
- Provide short-lived, narrowly scoped credentials rather than long-lived developer tokens.
- Require confirmation before package installation, destructive commands, permission changes and deployment actions.
- Review MCP or other tool integrations, hooks and generated workflow changes as security-sensitive code.
- Keep an audit record of the tool, relevant permissions and accountable human reviewer where your process requires it.
Agent instructions can alter future behavior, and tool access can affect systems outside the repository. A source-only review misses those controls.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Step 8: Record accountable approval
Assign one human owner to every AI-assisted change. The owner must understand the behavior, accept responsibility for correctness and maintenance, and explicitly approve the merge. AI-generated review comments can help locate suspicious lines, but an AI reviewer is not a substitute for human approval. OWASP’s concise rule is: “AI-generated code must have a human owner.”
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What the approval record should capture
- The intended behavior and affected trust boundaries
- Security-sensitive files or decisions that received additional review
- Independent tests added and automated checks run
- Dependency and provenance decisions
- Known limitations, follow-up work and operational owner
- The identity of the human approver and, where required, the AI tool used
Common failure patterns and recovery actions
“The diff is small, so we skipped review”
A one-line policy or configuration change can expose an entire service. Reclassify the change by impact and trust boundary, then review the surrounding configuration and deployment path.
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“The scanner is clean, so it is safe”
Scanners recognize known patterns and vulnerable components. They do not reliably determine whether a user is entitled to perform a business operation. Recheck authorization, state transitions and data ownership manually.
“The generated explanation says it is secure”
Treat the explanation as a map of claims to verify. Trace the actual code, configuration and tests; look for omitted assumptions and paths the explanation never mentions.
“We mocked the dependency to make tests stable”
Determine whether the mock removed the very behavior that matters, such as authorization, transactionality, parser limits or retry handling. Add an integration or contract test that exercises the real boundary in a safe environment.
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Stop and verify the intended library in the approved registry. Check provenance and API compatibility rather than guessing from names. Use an allowlist or internal proxy if repeated package confusion is a risk.
“The agent had broad access because it was faster”
Revoke exposed credentials, inspect command and network logs, rotate affected secrets and narrow permissions before continuing. Treat the event as a workflow security issue, not merely a code defect.
A pull-request checklist
- Intent, invariants and affected files match the issue or design.
- New endpoints, stores, permissions, dependencies and workflows are listed.
- Untrusted input is traced to each sink and validated before dangerous use.
- Authentication and object-level authorization are enforced at the service boundary.
- Queries are parameterized; dynamic execution and unsafe deserialization are absent or justified.
- Cryptography, secrets, IAM and deployment settings were reviewed for concrete configuration.
- Each dependency exists in the intended registry and has acceptable provenance.
- Tests include negative, boundary, concurrency and failure cases not supplied solely by the generator.
- Mocks do not bypass the behavior under review.
- Static, dependency, secret and applicable IaC or runtime checks passed or have documented exceptions.
- Assistant context, permissions, hooks and generated CI changes were examined.
- A human owner can explain the change and has approved it.
What the guidance does—and does not—claim
OWASP’s secure-review, AI-assisted-development and AISVS guidance provides controls for reviewing generated code; it does not establish a universal defect percentage for AI-generated software. NIST SP 800-218A, finalized on July 26, 2024, supplements SSDF 1.1 with practices and tasks for AI model development across the software life cycle. It is a development-framework reference, not a measured defect-rate study. Review decisions should therefore rely on the change’s risk, evidence and controls rather than an assumed average failure rate.
OWASP’s guidance is rolling material accessed October 13, 2026; the AISVS 1.0 appendix and the NIST publication date identify the versions cited here. Recheck your organization’s policies and tool behavior as those documents and products evolve.
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