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Sometimes. You may submit an AI-generated or AI-assisted patch only when the target project’s current rules permit it and you can meet its ordinary requirements for licensing, provenance, review, testing, disclosure and sign-off. There is no universal open-source rule. The human contributor—not the model—must understand every changed line, check for copied or incompatible material, run the relevant tests and answer reviewers’ questions. Some projects require a disclosure trailer or pull-request statement when AI supplied a substantial part of the change; others distinguish substantial generation from minor spelling or grammar help. An AI agent cannot make your legal certification or add a human Signed-off-by line.
What decides whether an AI-generated patch is allowed?
The repository’s current contribution policy controls the outcome. A permissive license does not automatically mean that every use of an AI tool is acceptable, because projects can impose additional process, disclosure, provenance or review requirements.
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- Repository instructions: Read
CONTRIBUTING.md, the project’s AI guidance, code-of-conduct rules and any pull-request template. - License and contribution terms: Check the project license, Developer Certificate of Origin (DCO) or Contributor License Agreement (CLA), and rules for copyright notices and third-party code.
- Tool terms: Confirm that the AI service permits the way you supplied input and the way you plan to redistribute output. Look for restrictions involving confidential code, ownership, output licensing, attribution or model training.
- Maintainer instructions: If the documents are silent or conflict, ask the maintainers before investing in a large change. Their answer may be narrower than the project’s general license.
Linux Foundation guidance states that “Code or other content generated in whole or in part using AI tools can be contributed to Linux Foundation projects,” provided contributors also satisfy tool terms, third-party-rights checks, attribution duties and project-specific rules. Individual projects under that umbrella can still set stricter conditions.
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Therefore, “open source” is not a blanket permission to submit unreviewed model output. Treat permission as conditional on the repository’s process and on your ability to make the same authorship, licensing and quality commitments required for any other contribution.
What must you check before opening the pull request?
Make the checks part of your normal contribution workflow, not an afterthought added after a reviewer asks where the code came from.
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Search the repository for AI, artificial intelligence, generative, LLM, bot, disclosure, provenance and DCO. Policies can live in CONTRIBUTING.md, a dedicated policy page, a pull-request template or commit-message guidance. Record the required wording and where it must appear; a disclosure in the description may not substitute for a required commit trailer.
Check input, output and redistribution terms
Review the AI tool’s current terms for restrictions on prompts, uploaded source code, output ownership, redistribution and training. Do not paste private contributor data, security reports or code covered by a non-disclosure obligation unless you are authorized and the tool’s terms permit it. A project’s license cannot cure a conflict between the tool contract and your intended use.
Inspect provenance and third-party material
Read the generated code rather than assuming it is newly authored. Search for distinctive comments, unusual boilerplate, long literal blocks and dependency snippets that may have been copied from a third-party project. Preserve required copyright and license notices, and document any dependency or example code you add. The Apache Software Foundation says contributors must disclose copyrighted material that is not their original creation, including material produced through generative tooling, even when the contribution is distributed under Apache-2.0 terms.
Review every changed line
Understand control flow, error handling, security assumptions, performance implications and compatibility behavior. Ask yourself what happens on malformed input, timeouts, retries, concurrent access, partial failure and upgrades. Apache Flink’s guidance puts the standard plainly: “Treat AI output as a draft. Run the tests, read the logic, and check the behaviour yourself before submitting; do not rely on the model’s claim that something works.”
Run the project’s checks
Use the same build, lint, type, unit, integration and documentation checks expected of a human-written patch. Add or update tests for the behavior you changed. If a test cannot run locally, state the limitation and provide the evidence you do have; never report a model’s assertion as a test result.
Prepare to explain the design
Maintainers may ask why an API, algorithm, dependency or error message was chosen. You must be able to explain the trade-offs without delegating the answer to the model. If you cannot reconstruct the reasoning or reproduce the result, the patch is not ready to submit.
Add the required disclosure and attestations
Use the project’s exact trailer, checkbox or pull-request statement. Keep legal attestations human: disclose AI assistance separately from a DCO or other certification.
How do major projects differ in their AI contribution rules?
The projects below illustrate why you must compare policies on more than a simple allowed-or-banned basis. Their guidance addresses different thresholds, syntax and legal obligations.
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| Project or policy | Is AI content allowed? | Disclosure trigger and format | Provenance, license and human obligations |
|---|---|---|---|
| Linux Foundation guidance | Yes, when tool terms, third-party rights, attribution and project rules are satisfied. | No single foundation-wide syntax; follow the individual project. | Check output and input rights, preserve required attribution and meet the project’s review and contribution process. |
| Apache Software Foundation | AI output can be contributed under the normal Apache process. | Exact syntax is not stated in the guidance summarized here. | Disclose copyrighted material that is not your original creation, including content produced by generative tooling; ICLA and Apache-2.0 obligations still apply. |
| Linux kernel | Only material compatible with GPL-2.0-only and the kernel’s contribution rules. | Follow kernel documentation; an AI agent must not create a legal sign-off. | Humans review and take responsibility, satisfy SPDX requirements and add their own Signed-off-by. The kernel states: “AI agents MUST NOT add Signed-off-by tags.” |
| Ansible | Generally permitted under its contribution process. | Explicit disclosure is expected when a significant part is taken from AI output without significant changes. Incidental spelling or grammar help is treated differently. | The author remains responsible for the submitted content and normal quality review. |
| Apache Flink | AI-assisted contributions are welcome under the same review standards as human-written code. | Use the disclosure checkbox and a Generated-by: <Tool Name and Version> trailer. |
Run tests, read the logic, check behavior and understand enough to answer reviewer questions. |
| GCC | LLM-generated contribution content is permitted when its process rules are followed. | The commit message must include an Assisted-by: tag. |
Only a human may provide Signed-off-by; the AI system cannot make that certification. |
| vLLM | AI-assisted changes must meet ordinary quality, testing and review standards. | Follow the project’s current disclosure instructions. | Do not submit a “pure agent” pull request without human validation. |
These policies can change. Check the live repository guidance immediately before submission, especially if you are working from an older clone or a remembered policy.
How much AI assistance has to be disclosed?
There is no cross-project percentage or universal threshold. The practical dividing line is whether the AI supplied material that materially shaped the contribution and was submitted with little substantial change.
Material generation
If a model produced a function, algorithm, test suite, documentation section or configuration that you kept substantially intact, treat that as material use. An explicit disclosure is the safer course when the project asks for one, even if you also rewrote portions of the output.
Incidental assistance
Some policies distinguish low-impact help such as correcting spelling, grammar or formatting. Ansible’s guidance treats incidental language assistance differently from a significant part of the contribution being taken from AI output. Do not assume every project uses that distinction; follow its stated threshold.
Mixed or iterative work
If you used an agent for planning, generated several alternatives, or accepted code in multiple small completions, describe the use accurately rather than trying to classify each prompt in isolation. When a project offers a checkbox and a trailer, complete both if instructed.
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Examples of project-specific syntax include Assisted-by: gpt-5.4 in Ansible’s examples, GCC’s required Assisted-by: tag, and Flink’s Generated-by: <Tool Name and Version> trailer. Copy the capitalization, punctuation and location required by the repository; a near match may not satisfy an automated check.
Can an AI agent add a Signed-off-by line or make a legal certification?
No. A DCO sign-off, CLA certification or similar statement is a human representation that the contributor has the required rights and accepts the stated obligations. An agent cannot possess that authority or truthfully make the certification for you.
The Linux kernel explicitly says “AI agents MUST NOT add Signed-off-by tags.” GCC likewise requires an Assisted-by: disclosure for LLM-generated content while reserving Signed-off-by for a human. You may use automation to prepare a commit, but inspect the final message and add your own sign-off only after you have reviewed and can stand behind the patch.
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Why can maintainers reject an AI-generated pull request?
Permission to use AI does not waive ordinary acceptance criteria. A maintainer can reject a patch for any of the following reasons:
- Policy failure: missing disclosure, wrong trailer, an incomplete checkbox or a contribution that violates a repository-specific restriction.
- Unclear provenance: recognizable third-party text, missing license notices or output whose origin cannot be explained.
- Insufficient review: the author cannot explain the design, edge cases or security implications.
- Broken behavior: failing tests, flaky tests, regressions, unsafe defaults, compatibility breaks or incomplete documentation.
- Process defects: missing DCO or CLA paperwork, incorrect branch targeting, unrequested generated files or failure to follow formatting and commit conventions.
- Pure-agent submission: an autonomous change submitted without meaningful human validation. vLLM specifically warns against “pure agent” pull requests without that validation.
The reason for rejection is usually the same reason a human-written patch would fail: the contribution does not meet the project’s quality or process bar. AI use can make provenance and accountability questions more prominent, but it does not create a separate guarantee of acceptance.
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What is a defensible workflow from generation to merge?
- Define the change narrowly. Write the issue, affected interfaces, compatibility target and tests you expect before asking for code.
- Use authorized inputs. Exclude confidential or restricted material unless you have permission and the tool terms allow it.
- Generate a draft, not a final patch. Keep prompts, outputs and tool/version details when the project requests provenance or when you may need to answer questions later.
- Reconstruct the implementation. Read and, where necessary, rewrite the output so you understand every branch, dependency and failure mode.
- Check originality and notices. Investigate distinctive passages and preserve any required attribution or license text.
- Run the project checks. Execute the documented build and test commands, add regression coverage and inspect the resulting diff for accidental files or broad formatting churn.
- Review as a maintainer would. Check API compatibility, security, performance, accessibility where relevant, documentation and upgrade behavior.
- Apply the project’s disclosure. Add the exact trailer, checkbox or statement and identify the tool/version when required.
- Make human attestations. Add DCO or other sign-off only as yourself and only after confirming the certification is true.
- Answer questions directly. Explain what the model contributed, what you changed, what you tested and any remaining limitations.
What should you do when the repository has no AI policy?
Silence is not the same as permission to skip disclosure or provenance checks. Follow the general contribution, license and DCO/CLA rules, disclose material AI assistance in the pull request, and ask maintainers whether they prefer a particular trailer or wording. Keep the disclosure factual and limited to what you actually used; do not claim that a model authored the entire patch if it only helped with a small part.
If maintainers give a project-specific answer, preserve that decision with the contribution record and follow it consistently for subsequent commits. Recheck the policy if the patch is moved to another repository, backported to a release branch or submitted under a different foundation project.
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Does using code completion count as AI-generated content?
It depends on the project’s threshold. A completion that supplies a substantial function or block is easier to classify as material AI assistance than a spelling correction, but policies differ; describe the use honestly and follow the repository’s own definition.
Should I disclose AI use if I rewrote all of the generated code?
Disclose it when the project asks for material-use disclosure or when the generated output materially influenced the design. A complete rewrite may change the provenance analysis, but it does not remove your duty to check for recognizable third-party material or to answer maintainers’ questions.
What records should I keep about an AI-assisted patch?
Keep the tool name and version, relevant prompts or summaries, the original output when practical, and your test results. These records help you explain provenance and reproduce decisions if a maintainer later asks how a distinctive section or dependency entered the patch.
Can I submit an AI-generated security fix privately to maintainers?
Use the project’s security-reporting channel rather than a public pull request when the vulnerability is not yet disclosed. The same human review, provenance and disclosure duties apply, but revealing prompts or source code to an external AI service may create additional confidentiality risks.
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Ask the maintainers which version of the policy applies and whether they want an updated trailer or disclosure. Do not silently amend attestations; explain any change in the pull-request discussion and keep the final commit history consistent with the requested process.
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