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Yes, you can use AI to help write or investigate a Linux kernel contribution, but you—not the tool—must understand, verify, disclose and take responsibility for the work. For meaningful tool-generated content, Linux kernel guidance calls for transparency about what tools did and how you tested the result, and the AI-specific guide gives an Assisted-by tag format. The ordinary patch, review and Developer’s Certificate of Origin (DCO) requirements still apply.
Which kernel guidance applies?
Two project documents address AI-assisted work from complementary angles. The AI Coding Assistants guide, in the next documentation branch as of October 13, 2026, gives AI-specific procedures and points contributors back to the standard development and patch-submission process. The Kernel Guidelines for Tool-Generated Content, in the latest documentation branch on that date, apply more broadly to generated work from tools, not only AI. The patch-submission guide also cautions that subsystem and maintainer trees may have additional workflow requirements.
These are guidance documents, not a promise that every subsystem handles a contribution identically. Follow the workflow used by the relevant subsystem, and check its current instructions as well as the project-wide guidance.
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When does tool use need disclosure?
The distinction is between routine mechanical assistance and meaningful content produced or shaped by a tool. The generated-content guidance puts spelling corrections, identifier completion, trivial pattern completion, mechanical renaming and formatting outside its scope. A tool-suggested fix, an AI-generated function, a file first generated by an assistant and then edited by a person, an AI-written changelog, a translation, or changes produced with Coccinelle are examples of work for which transparency is relevant.
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Tool-assisted detection or testing should also be mentioned in the changelog when it materially informed the contribution. In other words, disclosure is not limited to code that remains verbatim from a model: a tool’s role in finding a problem or testing a fix can matter too. If you are unsure whether assistance is substantial enough to disclose, the guidance says: “If in doubt, choose transparency and assume these guidelines apply to your contribution.”
| Use of a tool | How to treat it |
|---|---|
| Spelling fixes, formatting, mechanical renaming, or similarly routine help | The generated-content guidance treats these as out of scope for its disclosure expectations. |
| A generated function, a suggested fix, or a generated file later edited by a person | Disclose meaningful generated content and identify the affected portions. |
| Tool-assisted bug detection or fix testing | Note the assistance in the changelog when it contributed to the work. |
| AI-written changelog or translation | These are examples of meaningful tool-generated content; describe the tool’s role transparently. |
What to disclose, and where to put it
For meaningful tool-generated work, put useful context in the cover letter and changelog. The aim is to let reviewers see what was tool-assisted and where their attention may be especially useful. Include:
- The tool or tools used.
- The inputs supplied, such as prompts or relevant context.
- Which parts of the submission were generated or otherwise affected.
- How you tested the patch and what the tests established.
If one or a few prompts produced most of the content, include those prompts. For a long interaction, summarize the prompts and the kind of help requested rather than obscuring the tool’s contribution in a transcript. The amount of detail should make the contribution legible to reviewers, not substitute for an explanation of the code itself.
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The AI-specific guide gives this tag format:
Assisted-by: LLM [TOOL1] [TOOL2]
The bracketed names are optional specialized analysis tools used, with examples including Coccinelle, sparse, smatch and clang-tidy. The guide says not to list basic development tools such as git, gcc, make or editors. Separately, the general patch-submission guide says to use an Assisted-by tag when an advanced coding tool was used to create a patch and warns that omitting it may impede acceptance. Treat the tag as an acknowledgment, not a substitute for the explanation of what the tool did, the test evidence, or your own review.
Who signs off and accepts responsibility?
The human contributor signs off; an AI agent must not add a Signed-off-by tag. The AI-assistant guide states: “AI agents MUST NOT add Signed-off-by tags.” A human sign-off certifies the contributor’s relationship to the work under the DCO: the contributor created the contribution in whole or part and has the right to submit it, or otherwise holds the rights described by the DCO.
Before signing off, review all generated code, check licensing compliance and take responsibility for the complete contribution. An Assisted-by acknowledgment neither transfers that responsibility to a tool nor replaces your DCO sign-off. If you cannot explain the submitted code or establish that you have the right to submit it, do not treat a model’s output or a tag as a way around those obligations.
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What maintainers expect you to understand and defend
You are expected to understand the entire submission and respond to review comments. That includes code produced by a model, tool-influenced changes you rewrote, and the rationale and tests presented with the patch. The generated-content guidance puts it plainly: “You are expected to understand and to be able to defend everything you submit.”
Maintainer responses are discretionary. A contribution may be handled like any other, rejected, reviewed with extra scrutiny or testing, given lower priority, or met with requests for more information about the tools, model, training or contributor’s understanding. The more content a tool generates automatically, the more scrutiny a contributor should expect. If you cannot understand and defend the output, the guidance says not to submit it; a maintainer may reject a series without detailed review.
AI help does not change patch standards
A patch still needs a clear, reviewable technical case. Explain the underlying problem, its user-visible impact and the solution so reviewers can judge whether the change produces the intended behavior. Keep a patch focused on one problem and independently understandable. A convincing explanation matters even when the code is correct: reviewers need to know what behavior is being fixed and why the proposed change is appropriate.
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- Use the relevant subsystem’s established workflow and send patches through the project’s email-based process.
- Run
scripts/checkpatch.pland address applicable findings. The script is a guide, not a replacement for human judgment or review. - For a patch series, keep each patch buildable and functional; a series may be bisected at any point.
- Respond to review comments and revise or explain the patch as needed.
If a patch claims a performance or resource improvement, provide measurements you actually obtained and explain relevant trade-offs and how you measured the result. Do not present an unmeasured claim or a model-generated benchmark as evidence.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A practical workflow for an AI-assisted bug fix
The AI-assistant guide describes a sequence that keeps investigation, verification and submission connected. Use it as a checklist, adapting it to the subsystem’s process:
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- Start from an up-to-date kernel tree. Record the commit you used so reviewers can understand the code and conditions behind your investigation.
- Verify a nontrivial bug. Build a reproducer and confirm the behavior rather than treating a tool’s suggested diagnosis as proof.
- Write and inspect the fix. Review all tool-generated or tool-influenced code yourself, including its licensing implications and fit with surrounding code.
- Build and check the result. Verify the change without introducing new warnings, and run
scripts/checkpatch.pl. - Prepare a detailed commit message. Explain the problem and solution, include the required
Fixestag where applicable, and disclose meaningful tool involvement and testing. - Identify recipients. Use
scripts/get_maintainer.plto identify maintainers and mailing lists, then follow the relevant subsystem’s submission process. - State the limits of your verification. Explicitly say if you could not reproduce, build or test something; do not let an unverified step appear to have succeeded.
These steps do not guarantee acceptance. They give maintainers concrete evidence to assess and make gaps visible instead of leaving reviewers to infer what happened.
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How to report an AI-assisted bug or security issue
For an ordinary bug report
Separate a plausible diagnosis from a verified bug. A useful report gives maintainers enough information to reproduce and assess the behavior: the relevant version or commit, what happened, how to trigger it, whether you reproduced it, and what you tested. If a tool identified the issue or helped test a fix, note that role. The AI-specific guide directs contributors to the kernel threat model when deciding whether a report is a vulnerability or a regular bug.
For a suspected security vulnerability
Use the project’s private security reporting process described on the Security bugs page rather than posting a suspected vulnerability indiscriminately to public lists. Make the report concise and plain text. Lead with the affected version or commit, the problem, whether a reproducer exists, and the conditions that trigger it. Test the reproducer and proposed fix before reporting when possible, and remain available for follow-up questions and additional tests.
State verifiable impact rather than a chain of hypothetical consequences. The security guidance says: “Please stick to verifiable facts (e.g., ‘this bug permits any user to gain CAP_NET_ADMIN’) without enumerating speculative implications.” A report that cannot establish its central claim, or that gives maintainers no practical way to assess it, risks being ignored; the page cautions that “Failure to consider these points exposes your report to the risk of being ignored.”
Quick Recap
Questions to settle before sending the patch
- Can I explain every part of the change? If not, investigate further or leave it out.
- Was tool output meaningful? If it created or materially shaped content, or informed detection or testing, disclose that role in the appropriate submission text.
- Did I verify the claimed behavior? Describe what reproduced, built and tested successfully, and what did not.
- Is the contribution reviewable? Explain one problem and its impact, keep patches independently understandable, and provide the evidence behind claimed results.
- Have I signed off as the human contributor? Review licensing and DCO responsibilities; never let an AI agent sign off.
- Am I following the right route? Check subsystem-specific instructions and use the private reporting route for security issues.
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