You can use AI to help prepare a Linux kernel contribution, but you—not the AI—must understand, verify, disclose, sign off on, and take responsibility for the work. Meaningful tool-generated content calls for transparency, and an Assisted-by acknowledgment may be expected. The normal patch, testing, review, and security-reporting rules still apply; maintainers decide how to handle each contribution.
The project’s AI-specific guidance is in the AI Coding Assistants page, currently under the next documentation branch. Its broader Kernel Guidelines for Tool-Generated Content and patch-submission guide are under latest. These pages were reviewed on October 13, 2026; subsystem maintainers may set additional workflow requirements.
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What counts as AI-assisted kernel work?
The kernel project treats AI use as part of its broader guidance for tool-generated content, not as an exemption from the established contribution process. The important distinction is whether a tool supplied meaningful content or made a substantive contribution, rather than whether a tool was present somewhere in your workflow.
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Routine mechanics versus meaningful content
Routine assistance—such as correcting spelling, completing an identifier, filling in a trivial pattern, mechanically renaming symbols, or formatting code—is out of scope of the generated-content guidelines. By contrast, a suggested fix, an AI-generated function, a file first drafted by an assistant and then edited by a person, or an AI-written changelog is meaningful generated work. Translation and changes produced by tools such as Coccinelle also fall within the broader guidance.
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Editing generated code does not make its origin irrelevant: the guidance expressly includes a file initially generated by an assistant and later edited by a person. If it is unclear whether the assistance was substantial enough to disclose, the project advises choosing transparency. The general rule covers non-AI tools too, so the question is not limited to chatbots or code-generation models.
Finding and testing a problem also matters
Tool involvement is not limited to writing code. If a tool helped detect a problem or test a proposed fix, note that assistance in the changelog where the guidance applies. Keep a clear distinction between tool output and your own verification: a model suggesting that a bug exists is not evidence that the bug can be reproduced, and a generated patch is not evidence that it builds or behaves correctly.
What to disclose, and where
For meaningful tool-generated work, provide context in the cover letter and changelog. Reviewers need to know what the tool contributed so they can assess the change with an accurate understanding of its origin and where to focus review. The generated-content guidance recommends identifying the tools, the inputs given to them, the portions affected, and the testing performed.
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Describe the contribution, not just the tool name
A useful disclosure gives reviewers enough information to understand the extent of assistance. State which parts were generated, suggested, translated, or otherwise affected; describe the prompts or other inputs that shaped the result; and say what you did to review and test it. For code largely generated from one or a few prompts, include those prompts. For a long interaction, summarize the prompts and the kind of assistance instead of reproducing an unwieldy conversation.
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Do not imply that a tool did less than it did. If it proposed a fix that you substantially rewrote, say so; if you used it to draft the changelog rather than the code, identify that contribution. Reporting that a build or test was run is not the same as explaining the outcome. Include the actual testing performed and plainly identify what could not be reproduced, built, or tested.
Use the Assisted-by acknowledgment
The AI-specific guide gives this tag format:
Assisted-by: LLM [TOOL1] [TOOL2]Use the project’s prescribed acknowledgment in the patch context where applicable. The bracketed tool names are optional specialized analysis tools used; the guide’s examples include Coccinelle, sparse, smatch, and clang-tidy. Basic development tools such as git, gcc, make, and editors do not belong in that list. The patch-submission guide also says to use an Assisted-by tag when an advanced coding tool was used to create a patch, warning that omission may impede acceptance. See the current AI Coding Assistants and patch-submission instructions for the precise context and format relevant to your submission.
The broader guidance puts the default plainly: “If in doubt, choose transparency and assume these guidelines apply to your contribution.” — Kernel Guidelines for Tool-Generated Content.
Who signs off—and who is responsible?
The human contributor signs off on the patch. An AI agent must not add a Signed-off-by tag: only a human can legally certify the Developer’s Certificate of Origin (DCO), according to the AI Coding Assistants guidance.
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Your sign-off is a certification under the DCO that you created the contribution in whole or in part and have the right to submit it, or that you otherwise have the rights described by the DCO. It is not a claim that no tools were involved. Conversely, an Assisted-by acknowledgment records tool involvement; it does not transfer responsibility to the tool and cannot replace your own sign-off.
Before signing off, review all AI-generated code, check licensing compliance, and be prepared to take full responsibility for the contribution. If you cannot explain why the change is correct, what its effects are, and how it fits the surrounding code, you are not ready to certify and submit it.
Can maintainers reject an AI-assisted patch?
Yes. Contributors are expected to understand and defend everything they submit, whether or not a tool helped produce it. The generated-content guidance says maintainer responses are discretionary: a contribution may be handled like any other, receive extra scrutiny or testing, be given lower priority, or be rejected. A maintainer may also ask for more information about the tools, model, training, or the contributor’s understanding.
The amount of generated content can affect how much scrutiny to expect. If you cannot understand and defend the output, the guidance says not to submit it; a series submitted in that state may be rejected without detailed review. The project states: “As with all contributions, individual maintainers have discretion to choose how they handle the contribution.” — Kernel Guidelines for Tool-Generated Content.
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This makes disclosure necessary but not sufficient. A tag does not guarantee acceptance, and a maintainer’s review is not a substitute for the contributor’s own technical and licensing checks. Your responsibility is to present a reviewable change and answer questions about it; the maintainer decides whether the evidence and implementation meet the needs of that subsystem.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to prepare a reviewable AI-assisted patch
The standard kernel submission process remains in force. A patch needs to explain the underlying problem, its user-visible impact, and the technical solution so reviewers can judge whether the change does what it claims. Keep a patch focused on one problem and independently understandable. For a series, each patch should leave the kernel buildable and functional, because a series may be bisected at any point.
- Read the relevant guidance. Consult the AI-assistant page, the generated-content guidance, the patch-submission guide, and the documentation for the relevant subsystem. The project notes that subsystem and maintainer trees may have additional workflow requirements.
- Establish the code context. Work from an up-to-date kernel tree and record its commit. Read the surrounding implementation and relevant process documentation rather than relying on isolated keyword matches or an assistant’s summary.
- Verify the problem. For a nontrivial bug, try to reproduce it and record the conditions and evidence. Do not describe a suspected defect as confirmed if you could not reproduce it.
- Review and shape the change. Check every generated or tool-suggested line against the actual code, interfaces, and intended behavior. Make the patch solve one problem, remove unsupported assumptions, and ensure the explanation distinguishes measured facts from hypotheses.
- Build and test. Build and verify the change, check for new warnings, and use
scripts/checkpatch.pl. The script is a guide, not a substitute for human judgment. Record what you actually ran and anything you could not build or test. - Prepare the patch description and disclosure. Explain the problem, impact, and solution; describe meaningful tool involvement and inputs; report testing; and add the appropriate
Assisted-byacknowledgment. Add your own DCO sign-off, never an agent’s. - Submit through the established workflow. Identify the maintainers and mailing lists with
scripts/get_maintainer.pl, follow the relevant subsystem’s process, send patches by the established email workflow, and respond to review comments.
For a claimed performance or resource improvement, provide measurements you actually obtained and explain relevant trade-offs. Do not present a model-generated estimate as a benchmark or invent figures; the submission guide expects quantified claims to be supported by measurement and its conditions.
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A bug report is useful when maintainers can understand, verify, and act on it. AI-assisted reports still need concrete evidence: the affected version or commit, the problem, triggering conditions, whether a reproducer exists, and verifiable impact. A speculative report that has not been checked can consume maintainer time without establishing that a bug exists.
For a regular bug
The AI-assistant guide recommends reading the relevant process documentation, working from an up-to-date tree and noting its commit, and verifying nontrivial bugs with a reproducer. If you prepare a fix, build and verify it, check that it introduces no new warnings, run checkpatch, write a detailed commit message with a Fixes tag, and identify the appropriate maintainers and lists with scripts/get_maintainer.pl. State explicitly what you were unable to reproduce, build, or test rather than leaving reviewers to infer it.
Use the kernel threat model to help decide whether the issue is a regular bug or a potential vulnerability. Do not treat a tool’s security label as a determination: classify the concern using the project’s guidance and the facts you can verify.
For a potential security vulnerability
Follow the project’s private security reporting process in its Security bugs guidance rather than posting a potentially sensitive issue indiscriminately to public lists. The report should be concise and plain text. Lead with the affected version or commit, the problem, reproducer status, and triggering conditions; describe impact that can be verified rather than a chain of speculative consequences.
Test the reproducer and proposed fix before reporting where possible, and remain available for follow-up questions and additional testing. The security guidance warns that failure to address its reporting points risks a report being ignored. Its standard is concrete: “Please stick to verifiable facts (e.g., ‘this bug permits any user to gain CAP_NET_ADMIN’) without enumerating speculative implications.” — Security bugs.
A practical readiness check
Before sending an AI-assisted contribution, confirm each point that applies:
- You can explain and defend the whole patch, not only the lines you personally typed.
- You have reviewed generated code and checked that you have the rights to submit the contribution under the DCO.
- You have identified meaningful tool contributions, relevant inputs or prompts, affected portions, and actual testing in the cover letter or changelog as appropriate.
- You have included the appropriate
Assisted-byacknowledgment for advanced coding-tool involvement and signed off as the human contributor. - The patch explains one problem and its impact, follows subsystem workflow, and is buildable and functional at each step of a series.
- Claims about correctness, performance, or security are supported by evidence, with uncompleted tests and unresolved reproduction attempts clearly identified.
- You are prepared to answer review questions and make requested changes.
The current documentation branches matter when consulting these instructions: the AI-assistant and security pages cited here are under next, while generated-content and patch-submission guidance is under latest. Because these pages and subsystem workflows can change, check the applicable kernel documentation and maintainer instructions when preparing a real submission.
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