Review AI-generated code to the same standard as any other code: understand what it changes, verify that it meets its requirements, examine its security and operational effects, and accept it only when a responsible developer can explain and own it. A passing test suite or clean scan is useful evidence, not proof that a change is correct or safe.
Who is responsible for AI-generated code?
The developer who approves and commits a change remains accountable for its correctness, security, and maintainability, whether a person or an AI system wrote it. OWASP’s Secure Coding with AI Cheat Sheet puts that responsibility plainly: “Every AI-assisted change should be reviewed, approved, and attributable to a developer who is responsible for its security and maintainability.”
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That means a reviewer needs more than a plausible explanation from an assistant or a green checkmark in a pull request. The responsible developer should understand the design well enough to identify what it does, what assumptions it relies on, and what could go wrong. OWASP’s Top 10:2025, in “X03:2025 Inappropriate Trust in AI Generated Code (‘Vibe Coding’),” similarly says: “You should be able to read and fully understand all code you submit, even if it is written by an AI or copied from an online forum. You are responsible for all code that you commit.”
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How much review does the change need?
Use risk to decide where to spend the closest attention, not whether to review at all. A small, isolated change with clear requirements may need a narrower investigation than a change that processes sensitive data or affects production access. No universal score or tool ranking establishes that code is safe; human review, tests, and automated analysis find different kinds of problems.
#1 Best Overall
| Review priority | Examples of what to examine closely | Why it matters |
|---|---|---|
| Higher | Authentication or authorization, secrets, sensitive data, public-facing input, file or network access, privileged automation, deployment and migration changes | Errors may expose data, grant unintended access, execute untrusted actions, or disrupt production. |
| Also in scope | Dependency manifests, lockfiles, build scripts, CI workflows, repository or agent instruction files, configuration, generated code | These can change what is installed, executed, trusted, or deployed even when application source code looks routine. |
| Lower relative impact, still review | Isolated internal behavior with clear requirements and limited access to sensitive systems | Smaller impact can justify a more focused review, but does not remove the need to understand and test the change. |
This is a practical prioritization derived from the risks OWASP identifies for AI-assisted development and the lifecycle controls in NIST’s DevSecOps Notional Reference Model; it is not a formal risk-scoring standard.
What should you check in an AI-written pull request?
1. Establish the intended behavior and an owner
Before reading individual lines, identify the user or system behavior the change is meant to deliver, the relevant requirements, and the developer responsible for accepting it. Ask the author to explain the solution in their own words. If a critical section cannot be explained, treat that as an unresolved review issue rather than assuming the generated code is correct.
2. Read the whole diff in repository context
Compare the implementation with the stated scope, then read enough surrounding code to understand callers, data flow, error handling, and project conventions. Look for unexpected or unrelated edits, not just changes in the main source file. Include dependency manifests and lockfiles, build scripts, generated files, deployment configuration, CI workflows, and repository or agent instruction files. OWASP treats rules files as security-critical configuration and recommends review requirements when they change.
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For an agent-assisted change, also consider what the agent could read and do. Repository files, issue descriptions, pull-request comments, and external content may influence its output; an agent may also have access to tools, files, or network operations. Treat that context and the resulting code as untrusted until checked. OWASP describes indirect prompt injection and excessive CI-agent permissions as risks in the development loop.
3. Trace inputs, permissions, and trust boundaries
Follow untrusted data from where it enters the system to the operations it can affect. Check whether validation, encoding, authentication, and authorization happen at the right points. Examine file and network access, secrets handling, external dependencies, logging, and error paths. Ask whether an input can cross into a sensitive operation without an appropriate check, or whether an error response exposes information it should not.
For code created with an agent, inspect the agent’s permissions and actions as well as the application code. Consider whether untrusted issue or pull-request content could have steered it, whether its access was broader than necessary, and whether it made unexpected file or network changes. An agent’s output is not trustworthy merely because it came from a workflow inside the repository.
4. Verify behavior, edge cases, and failure paths
Compare what the code actually does with the requirement, including normal inputs, boundary conditions, invalid input, failures, retries, and compatibility with existing callers. Where relevant, examine concurrency, state transitions, and effects on existing data. Run the tests that make sense for the change, but inspect the tests rather than relying only on a pass result.
- Do the tests assert meaningful outcomes, rather than merely exercising code?
- Do they cover invalid input and important failure cases as well as the expected path?
- Do they preserve existing behavior that callers or users depend on?
- Are the tests independent enough to catch the defect the change is meant to prevent?
OWASP cautions against treating AI-generated tests as security proof or test pass rates as a measure of confidence. Tests can be incomplete, encode the same mistaken assumption as the implementation, or miss risks outside their assertions. Passing tests are one layer of evidence, to be considered alongside code review and security analysis.
5. Review dependencies and run independent security checks
Check the identity and version of each added or changed dependency, its provenance, and whether known issues apply. Do not assume the model knows current vulnerability disclosures or that a package it suggested exists and is the intended package. Review build and installation behavior too: changes to scripts, workflows, or configuration can affect the software supply chain even when the application code itself appears safe.
Use the team’s secure-coding standards and suitable analysis tools as complements to manual review. Static analysis and other security checks can help identify issues, but they do not replace scrutiny of security-critical logic, permissions, or assumptions. OWASP recommends manual review and security tooling; NIST’s Secure Software Development Framework (SSDF) describes code review and analysis as practices for identifying vulnerabilities.
6. Assess maintainability and operational effects
Ask whether another developer could understand and safely change the implementation. Look for duplicated logic, unnecessary abstraction, unclear names, hidden side effects, and configuration that is brittle or difficult to operate. Check whether the change needs documentation or operational notes, and consider logging, observability, data migration, and rollback where relevant.
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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteThese are practical review questions, not a claim that a particular source prescribes each item as a formal checklist. Their purpose is to test whether the accepted change can be operated and maintained by the team that owns it.
7. Record findings and approve deliberately
Describe review findings clearly enough that the author can reproduce or understand them, and request changes when a material concern remains unresolved. Approval should be an intentional decision by the responsible developer, not an automatic consequence of successful checks.
For automated or agentic workflows, limit credentials to the access needed, isolate execution, log actions appropriately, and require approval before sensitive writes or deployment actions. NIST’s DevSecOps Notional Reference Model places AI-generated output within established peer review, security validation, automated testing, and approval workflows; it does not make generated output an exception to them.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do review methods fit together?
Use multiple methods because each offers different evidence. Peer review can assess intent, context, and design; tests can check specified behavior; security analysis can flag classes of defects; dependency and configuration review can uncover supply-chain or deployment changes. None establishes safety on its own, and the NIST model supports combining these controls rather than substituting one for another.
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NIST SP 800-218 Rev. 1, the initial public draft of SSDF version 1.2, was published on December 17, 2025. It remains a draft, not a final standard. NIST SP 800-218A is a final July 2024 community profile that adds AI-model-development practices to SSDF 1.1; its scope is AI model development, so it should not be mistaken for a dedicated checklist for reviewing AI-generated application code.
The official sources cited here do not establish a specific percentage by which AI-generated code is more or less secure than human-written code. The useful conclusion for a reviewer is not a comparative statistic: assess the actual change, its evidence, and its consequences.
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