Yes, you can submit code written with an AI assistant to the Linux kernel, but the submission is treated as a human contribution. You must understand and review every proposed change, verify its provenance and GPL compatibility, add the correct SPDX metadata, certify the Developer Certificate of Origin (DCO) with your own Signed-off-by line, disclose meaningful tool assistance, and provide credible build, test, and reproduction evidence. An AI system cannot sign the patch, act as its author in the DCO chain, or submit reports on your behalf.
Kernel maintainers retain discretion over how much scrutiny an AI-assisted patch receives. A technically correct patch can still be rejected if you cannot explain it, cannot establish licensing compliance, or cannot show that a reported bug and its fix were verified.
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AI assistance does not create a separate kernel contribution process
The Linux kernel’s normal contribution rules still apply when a chatbot, code-completion service, or automated analysis tool helped create a patch. The relevant question is not whether a model touched the code; it is whether the human contributor can stand behind the complete change under the project’s review, licensing, and DCO requirements.
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsThe official AI Coding Assistants guidance puts the responsibility on the person submitting the patch. You are expected to inspect the entire diff, understand its behavior and side effects, check that it follows kernel conventions, and answer maintainers’ questions. Treat generated code as an untrusted draft, not as an authority. Plausible-looking output can contain incorrect locking, lifetime, error-path, ABI, or hardware assumptions that are difficult to spot if you review only the lines the assistant changed.
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Kernel maintainers may handle an AI-assisted change normally, ask for additional testing or explanation, or decline it. The Tool-Generated Content guidelines state: “As with all contributions, individual maintainers have discretion to choose how they handle the contribution.” That discretion covers both the technical quality of the patch and the quality of its provenance and disclosure.
Licensing: every submitted file needs a defensible GPL-compatible status
The AI Coding Assistants guidance requires submitted code to be compatible with GPL-2.0-only and asks contributors to use appropriate SPDX license identifiers. The kernel as a whole is GPL-2.0, while particular files can use compatible licenses or documented dual-license expressions. The license of an output is not automatically settled merely because an AI tool generated it; you still have to examine where the material came from and whether it can legally be included.
Use precise SPDX identifiers
SPDX identifiers are machine-readable declarations placed in source-file headers. They make the intended license unambiguous to automated tooling and reviewers. Add the exact expression required for the file rather than a vague phrase such as “GPL” or “open source.” If you modify an existing file, preserve its established licensing expression unless the project’s rules and the file’s provenance support a deliberate change.
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User-space API headers can use a special expression, GPL-2.0 WITH Linux-syscall-note, where that is the established licensing choice. Do not replace it with a generic GPL identifier simply because an assistant suggested one. A header’s licensing and ABI role are separate review concerns, so check both.
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Investigate copied or suspicious output
Before submitting, compare generated fragments with existing kernel code and with any examples or libraries that influenced the assistant’s answer. Remove material whose origin you cannot establish or whose license is incompatible. If a tool reproduces a non-kernel project’s distinctive implementation, comments, or code structure, stop and resolve the provenance through your organization’s legal process before placing it in a patch. The project guidance does not promise a universal answer to every copyright question in every jurisdiction.
Authorship and the Signed-off-by requirement
The DCO is certified by a human in the Signed-off-by chain. The official wording is unequivocal: “AI agents MUST NOT add Signed-off-by tags. Only humans can legally certify the Developer Certificate of Origin (DCO).” Your own Signed-off-by: Your Name <[email protected]> line means that you have reviewed the contribution, have the right to submit it under the project’s terms, and accept responsibility for it.
Do not list the model as a DCO signer, and do not describe it as a substitute author. An assistant can be acknowledged as a tool that contributed to the work, but it cannot make the legal certification. You remain responsible for the generated portions, the portions you edited, and the way they interact with the rest of the kernel.
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When AI use must be disclosed
The Tool-Generated Content guidelines apply when a meaningful amount of a kernel contribution was not written by a person in the Signed-off-by chain but was created by a tool. Examples include an assistant-generated function, a complete source file produced by a tool and then cleaned up by hand, a generated changelog, or a translated changelog.
Usually trivial assistance
Simple spelling corrections, identifier completion, mechanical renaming, and routine formatting are generally outside the disclosure requirement. Disclosure is still harmless when it helps a reviewer understand how the patch was produced, but do not imply that every keystroke requires an attribution line.
What to put in the cover letter or changelog
For meaningful generated content, provide enough information for a maintainer to reproduce your review and assess risk:
- Name the AI or other generation tools used.
- Include the prompts or inputs, or a concise summary when the interaction was too long to reproduce verbatim.
- Identify the files, functions, or sections that were generated or materially shaped by the tool.
- Describe how you reviewed and tested those portions, including tools and configurations.
- State plainly what you could not build, run, reproduce, or otherwise verify.
Disclosure is not an apology and does not transfer responsibility to the tool. It gives maintainers context about provenance and helps them decide whether the normal review path is sufficient.
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Use the Assisted-by tag correctly
For meaningful AI or specialized analysis assistance, the AI Coding Assistants page gives this format:
Assisted-by: LLM [TOOL1] [TOOL2]
Replace the bracketed names with the tools that actually contributed. Keep the tag distinct from Signed-off-by: the former records assistance, while the latter is your human DCO certification.
Specialized analysis tools such as coccinelle, sparse, smatch, and clang-tidy may be listed when they materially informed the change. Basic development tools such as git, gcc, make, and text editors should not be listed as assisted-by contributors. A compiler invocation or an editor’s autocomplete does not, by itself, constitute the kind of meaningful generated content the tag is meant to record.
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- Read the complete diff. Do not limit review to the hunk that the assistant displayed. Check callers, locking rules, reference counting, memory ordering, error unwinding, user input validation, integer widths, and interactions with existing configuration options.
- Trace every non-obvious line to a reason. Be able to explain why a value, barrier, timeout, callback, or API is correct. If you cannot explain a line, do not submit it until you can.
- Check provenance. Search for copied snippets, generated comments, and code patterns that may have come from outside the kernel. Remove or replace material you cannot license under the kernel’s terms.
- Verify licensing metadata. Confirm the SPDX expression in each new source file and preserve any special expression required by an existing file or UAPI header.
- Run the project’s validation tools. Build the affected configurations, run relevant tests, and use appropriate static checks. Resolve warnings rather than treating them as unrelated noise.
- Prepare a truthful record. Add your own Signed-off-by line only after confirming the DCO requirements. Add an Assisted-by line when the assistance was meaningful, and describe the tools, prompts or summaries, affected areas, and verification in the cover letter or changelog.
Extra requirements for AI-assisted bug reports and fixes
AI can help identify a possible defect, but an unverified report may be ignored. The assistant must not send a bug or security report itself. You, the human reporter, must review the result and use the normal kernel communication and disclosure process.
A defensible bug-fix sequence
- Read the relevant kernel process documentation before acting on the model’s suggestion.
- Record the exact commit ID or tree state in which you observed the behavior.
- Attempt to reproduce a non-trivial bug. Capture the conditions, configuration, logs, and failure mode rather than relying on the assistant’s description.
- Write the smallest well-justified fix, then build and verify it in the configurations affected by the change.
- Run
checkpatch.pland address its applicable findings. Avoid introducing build warnings. - Commit with a detailed message and an appropriate
Fixes:tag that identifies the introducing commit. - Identify the responsible maintainers and mailing lists, then submit through the normal process.
- State explicitly what you could not build, test, reproduce, or inspect. Do not imply coverage that you did not obtain.
A model’s confidence is not a reproducer, and a generated stack trace is not test evidence. Maintainers need the human-observed facts that let them reproduce and evaluate the change.
How maintainers are likely to evaluate an AI-assisted patch
There is no published acceptance rate or authoritative statistic for AI-generated code in the kernel. Review instead turns on several practical questions.
| Decision area | What a maintainer needs to see | Warning signs |
|---|---|---|
| Provenance and disclosure | Tools, prompts or summaries, affected portions, and a clear account of review | Generated code is hidden or its origin cannot be explained |
| DCO and human understanding | A human Signed-off-by line and an author who can explain the complete patch | The model is presented as signer, author, or decision-maker |
| GPL/SPDX compatibility | Defensible provenance and precise SPDX metadata for each file | Copied snippets, vague license claims, or missing identifiers |
| Reproducibility and testing | Commit ID, reproducer where relevant, builds, checks, and stated limits | Only a model-generated diagnosis or unverified test claim |
| Reviewability and maintainer time | Focused changes, detailed rationale, and responses to questions | Large opaque rewrites that the submitter cannot walk through |
Maintainers can ask for more detail, additional testing, information about model training, or a different workflow. If the contributor cannot meet that request, the patch may not be accepted even when some generated lines appear technically sound.
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Submission checklist
- Review every line and be ready to explain the behavior and trade-offs.
- Check generated material for copied or incompatible content.
- Use the correct SPDX-License-Identifier expression in each new source file.
- Add your own Signed-off-by only when you can truthfully certify the DCO.
- Add
Assisted-by: LLM ...for meaningful AI or specialized analysis assistance. - In the cover letter or changelog, document tools, prompts or summaries, affected sections, and testing.
- For bug fixes, include a reproducer when possible, the relevant commit ID, build and run checks, a detailed message, and a Fixes tag.
- List what you could not test or reproduce.
- Use the normal maintainer and mailing-list workflow.
- Never allow an AI system to submit a patch, send a report, or communicate autonomously in your name.
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