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Do not decide whether to merge a pull request by trying to prove who—or what—wrote it. Treat AI-assisted code like any other third-party code whose origin is uncertain: understand the entire change, verify its dependencies, run the project’s tests and security checks, and require stronger review for high-risk areas. AI detectors and stylistic clues can prompt closer scrutiny, but they cannot establish authorship or replace review.
The maintainer’s goal is not to punish AI use. It is to make sure the final patch is understandable, tested, secure, properly licensed, and consistent with the project’s contribution rules. If the contributor cannot explain or validate a part of the change, pause the merge and ask for a correction or rewrite.
Can you reliably tell whether a pull request was written by AI?
Usually, not from the code alone. Verbose comments, repeated patterns, a sudden large diff, or code that feels unlike the project may justify a closer look, but none proves that an AI tool produced it. A developer can write those patterns, and AI-assisted code can look ordinary after editing.
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A contributor’s disclosure is stronger evidence of provenance than a stylistic guess, but provenance is not a quality verdict. Disclosure does not make a change safe, and lack of disclosure does not establish that a change is AI-generated. Review the code on its merits and follow the repository’s policy for documenting AI assistance.
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Automated AI scanners should be treated as risk indicators, not authorship judges. GitHub documents limitations and false positives for its AI Scan features; its findings are advisory and do not currently provide merge-blocking ruleset enforcement. Its separate AI applications scanning feature is a public-preview detector for risks such as indirect prompt injection, sensitive-information disclosure, and excessive agency. Those are distinct security checks, not a way to certify that a human or an AI wrote a patch.
No credible prevalence or detector-accuracy statistic is established here. Do not use an unsupported percentage to justify blocking a contribution or trusting a detector.
What should a maintainer verify before reviewing the code in detail?
Start with the project’s rules and the contribution’s provenance, then establish exactly what changed. This prevents both unfair assumptions about AI use and missed requirements that apply to every contribution.
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- Look for requirements about AI disclosure, contributor understanding, testing, licenses, intellectual property, and any required sign-off or certification.
- Apply the same project standards whether the contributor used an AI tool, copied code from elsewhere, or wrote the patch unaided.
- If the project requires disclosure, ask for it in the location the policy specifies. Do not invent a new requirement for one pull request.
Existing open-source policies offer useful patterns. LLVM requires contributors to read and review all LLM-generated code or text before asking other project members to review it, and says, “The contributor is always the author and is fully accountable for their contributions.” Kubeflow requires AI-generated code to be built, linted, and tested locally before a pull request is opened; it also requires disclosure for substantial AI generation or refactoring and preserves its quality, security, DCO, license, and IP requirements. The Open Source Robotics Foundation (OSRF) recommends recording tool names and versions in commit messages and pull-request descriptions, with a durable, searchable format such as Generated-by:.
Ask a neutral provenance question
If the project’s process permits it, ask whether an AI tool materially generated or refactored the change. Keep the question factual: the answer helps you apply project policy and understand the contribution history; it is not a substitute for inspecting the patch. Check the pull-request description and commit messages, too, but do not infer authorship from a missing disclosure unless the repository’s rules make disclosure mandatory.
Establish the scope of the patch
Read the pull-request description and compare them with the actual diff. Identify changed files, new packages, generated files, configuration edits, tests, and behavior changes. Note unrelated edits or unexpectedly broad changes early, so they can be separated rather than obscuring the review.
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How do you review an AI-assisted pull request safely?
Review the patch in context, not as an isolated block of generated text. Follow the code through its callers, configuration, tests, and error paths, and check every new dependency before deciding whether the change is ready.
1. Trace the change through its context
- Read each changed function and inspect where it is called, what inputs it receives, and what assumptions callers make.
- Check related tests, configuration, error handling, and any behavior that occurs when a request or operation fails partway through.
- Compare the implementation with nearby code for naming, control flow, error conventions, and project-specific patterns.
- Check that comments and documentation describe what the code actually does—not what the author intended it to do.
Look for duplicated helpers, unused branches, inconsistent naming, impossible fallback logic, swallowed errors, unsafe defaults, and comments that do not match implementation. These are useful review prompts, not evidence that a tool wrote the code. The same defects can appear in manually written patches.
2. Verify every new package
For every added dependency, verify that the package exists, is the intended package, is maintained, has a license acceptable to the project, and is necessary for the change. A plausible-looking import or package name is not proof that it points to the intended software. Check for typosquatting or package confusion, unnecessary transitive dependencies, and a mismatch between the requested API and the package’s actual purpose.
Use the project’s software-composition analysis and dependency-scanning process where available. OWASP specifically calls for checking every new dependency and using software-composition analysis to identify package-level risks. If a small change introduces a broad dependency, ask why it is needed and whether the project’s existing libraries can do the job.
3. Build and test the actual behavior
Build and lint the project, run focused tests for the changed behavior, and run the full regression suite where practical. A passing test suite is evidence that tested cases work; it is not proof that untested edge cases, security properties, or licensing concerns are resolved. For changed behavior, inspect whether tests cover expected inputs, boundary conditions, failure paths, and regressions.
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Kubeflow’s policy makes local build, lint, and test a prerequisite before opening a PR. Even when a repository does not impose that specific timing, maintainers should require meaningful test evidence before merge. If the contributor cannot run the checks, ask what blocked them and decide whether another project-approved environment can provide equivalent validation.
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Use the repository’s established static analysis or CodeQL configuration, dependency and license scanning, and secret scanning. OWASP recommends applying security gates to all code regardless of origin; AI use is not a reason to skip them, and human authorship is not a reason to assume a patch is safe.
GitHub AI Scan can add advisory findings to eligible pull requests, but its findings are not currently merge-blocking ruleset enforcement. Do not treat a clean scan as a security approval or a finding as proof of AI authorship. AI applications scanning is a separate public-preview detector for application risks such as indirect prompt injection, sensitive-information disclosure, and excessive agency; it does not replace ordinary code review or repository security gates.
5. Ask the contributor to explain the design
Ask about the important design choices, assumptions, and edge cases—not whether the contributor can recite every line from memory. For example, ask why a dependency is needed, how an error path behaves, or what test would fail if the implementation regressed. An author should be able to explain the behavior and make a correction when review exposes a gap.
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Which code deserves elevated review?
Use a higher bar when a change touches code where a subtle defect could compromise access, data, or infrastructure. The same process applies to all changes, but sensitive areas warrant reviewers with relevant expertise and more focused tests.
- Authentication and authorization: verify identity checks, permission boundaries, and denial behavior.
- Parsing and deserialization: examine malformed, oversized, and unexpected inputs, as well as validation and resource handling.
- Cryptography: require a reviewer who can assess the chosen primitives, their use, and relevant failure modes.
- Filesystem and network operations: inspect path and destination handling, access controls, timeouts, and error behavior.
- CI and automation: check what code or credentials workflows can access, what events trigger them, and whether changes broaden privileges.
These categories are not evidence that an AI-generated patch is uniquely risky. They are areas where a mistake can carry unusually high impact, regardless of authorship.
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How do you clean up generated code without losing the useful change?
Keep the smallest coherent patch that solves the stated problem. Remove speculative structure and unrelated edits, correct invalid assumptions, and add tests for defects found in review. The result should fit local conventions and be clear enough for another maintainer to explain and maintain.
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Remove complexity the project does not need
- Delete duplicated helpers, unused branches, and speculative abstractions that do not serve the current change.
- Replace hallucinated or incorrect APIs with verified project APIs, or remove the code path if it is unnecessary.
- Align names, error handling, and control flow with established local conventions.
- Remove or rewrite comments that promise behavior the implementation does not provide.
Reduce dependency and diff risk
Pin, replace, or remove a dependency according to the project’s dependency policy. Do not keep a package solely because it appears in generated code: it must be real, suitable, maintained, license-compatible, and justified by the change. If a pull request mixes the requested fix with unrelated generated edits, ask the contributor to split those changes into separate commits or drop them.
Turn review findings into regression protection
When review finds a bug, add a test that would catch that behavior in the future, where the project’s test design makes that practical. Then rerun the relevant checks against the cleaned patch. A correction made only in a comment or explanation is not a substitute for correcting the implementation.
How should you decide whether to merge, request changes, or close the pull request?
Merge only when the final patch is understandable, tested, security-checked, license-checked, and compliant with repository policy. The decision should rest on the state of the code and the contributor’s ability to stand behind it—not on a guessed authorship verdict.
| Review outcome | Use it when | Maintainer action |
|---|---|---|
| Merge | The change is understood, passes the required checks, has acceptable dependencies and licensing, and follows project policy. | Complete the project’s normal approvals and merge process. |
| Request changes | A fix may be viable, but there are unanswered design questions, failed checks, unnecessary complexity, or missing validation. | Describe the concrete issue and the evidence needed to resolve it, such as a corrected API call, dependency rationale, or test. |
| Close | The contributor cannot or will not explain, test, or correct a material problem, or the patch cannot meet project requirements. | Give a specific reason tied to the code or policy, rather than presenting an authorship guess as proof. |
Disclosure is one part of provenance and policy compliance; it is not a substitute for tests, security analysis, dependency verification, license review, or human understanding. Conversely, a suspicion that AI was used is not itself a technical defect. Keep the decision anchored to verifiable requirements and the submitted patch.
What should a good project policy say about AI-assisted contributions?
A useful policy assigns responsibility, explains when disclosure is required, and preserves the checks that apply to every contribution. It should help contributors submit reviewable work without suggesting that naming a tool makes the code trustworthy.
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| Policy pattern | Practical requirement |
|---|---|
| LLVM | Contributors review generated code or text before requesting review and remain its accountable authors. |
| Kubeflow | Contributors build, lint, and test locally before opening a PR; disclose substantial AI generation or refactoring; and retain quality, security, DCO, license, and IP obligations. |
| OSRF | Contributors record tool names and versions in commit messages and PR descriptions, using a durable, searchable format such as Generated-by:. |
Projects can adapt these patterns to their own review process. A policy should define what counts as material or substantial use if that distinction affects disclosure, identify the durable record to use, and state that contributors remain responsible for all submitted code and text. It should also clarify whether documentation, tests, configuration, or other generated material is in scope.
What should maintainers consider when choosing AI review tools?
Compare tools by what they inspect and how findings affect the workflow; do not treat an “AI detector” label as a guarantee of coverage. The right choice depends on the repository’s languages, threat model, contributor process, and tolerance for advisory findings.
- Coverage: Which languages and frameworks are analyzed? Does the tool inspect only pull requests or the repository more broadly?
- Enforcement: Are results advisory, or can the project enforce a required check? Confirm current behavior rather than assuming a finding blocks merging.
- Risk areas: Does it cover dependencies, secrets, and licenses, or only a narrower class of findings?
- Contributor workflow: How does it behave with forks and Dependabot pull requests?
- Data handling: What data is processed, and what are the tool’s retention practices?
- Operational fit: What are the costs and licensing terms? How are false positives handled, and can reviewers audit the result?
Availability, licensing, preview status, and coverage can change. Verify current product documentation and repository settings before relying on a feature in a merge policy. A scanner is one control in a review system, not a replacement for dependency checks, tests, security expertise, or an accountable human reviewer.
Frequently Asked Questions
Does using AI for autocomplete need to be disclosed in an open-source pull request?
That depends on the project’s policy and how it defines material or substantial assistance. Some policies focus on substantial generation or refactoring rather than every suggestion; contributors should follow the repository’s stated threshold instead of assuming one universal rule.
Does AI-assisted code include generated tests and documentation?
It can: project rules may cover generated text as well as code, and LLVM’s policy explicitly refers to LLM-generated code or text. Check whether the repository’s disclosure and review requirements include tests, documentation, configuration, or other non-source files.
Does AI use change a pull request’s license or intellectual-property obligations?
AI involvement does not by itself resolve whether a contribution satisfies a project’s license or intellectual-property requirements. Apply the repository’s normal rules and seek appropriate project or legal guidance if a concrete rights question arises.
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