LLMs can make it cheaper to draft a patch, issue, or security report, but they do not make it cheaper to establish that the work is correct, safe, properly licensed, and worth merging. That verification still takes maintainer time. AI tools are already part of many respondents’ open source workflows, but the available evidence does not show one uniform effect on maintainers’ workload or project outcomes.
How common is AI use in open source work?
In the 2024 Open Source Survey, 72% of respondents said they use AI tools such as GitHub Copilot for coding or documentation. Among respondents who contribute to AI projects, 73% said they use AI tools; at the same time, 74% of respondents had never contributed to AI projects. These are survey results, not estimates of every maintainer, project, or region.
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The survey also asked, “When thinking about whether to contribute to an open source project, how important are the following things?” Security featured prominently: 82% of respondents considered secure-by-design important when deciding whether to use a project, and 62% considered it important when deciding whether to contribute. Those figures make security relevant not only to code review, but also to whether people trust and choose a project.
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AI can affect more than how code is written. Contributors may use it to draft documentation, issues, pull requests, reviews, or security reports; maintainers may use AI tools in their own workflows as well. A 2026 preprint examining qualitative material from 67 visible open source projects describes governance questions spanning contribution workflows and platform infrastructure. It is emerging research, not a settled finding about all projects.
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The operational tension is straightforward: generating more submissions may not increase a project’s ability to assess them. As the preprint authors put it, “cheaper generation does not mean cheaper review.” A plausible patch can still be incorrect, incompatible with project conventions, insecure, or difficult to trace to its origin. The available sources do not establish a single causal estimate for AI’s net effect on maintainer workload, burnout, software quality, or security.
What should a project’s AI contribution policy cover?
A useful policy does more than say yes or no to AI. Projects can make different choices according to their risk, review capacity, and community, while making expectations clear to contributors. The OpenSSF AI/ML Security Working Group identifies risks including privacy and secret leakage, data poisoning, prompt injection, licensing, and adversarial attacks; it also considers ways AI can improve security.
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- Disclosure: Say what contributors should disclose about AI assistance, and whether that applies to code, documentation, tests, or other submissions. Disclosure gives reviewers context; it does not establish that a contribution is correct.
- Accountability: Require a human contributor to understand and stand behind submitted work, answer review questions, and make revisions. A tool should not become the accountable party.
- Verification proportional to risk: Specify the evidence expected for a change, such as tests, reproducible steps, or security review. A typo fix and a change to authentication or cryptography do not need identical scrutiny.
- Provenance and licensing: Set expectations for explaining where code or other material came from and for checking whether it can be used under the project’s license. Do not assume that AI assistance resolves those questions.
- Privacy and secrets: Explain what project data, personal information, credentials, or unpublished vulnerability details must not be sent to external tools, and point contributors to approved tools or processes where applicable.
- Capacity and triage: Decide how the project will handle a higher volume of submissions or reports, including what it can review and how it will communicate delays. A rule that creates review obligations beyond available capacity may not be sustainable.
These are practical policy dimensions, not a standardized framework endorsed by every project. A project can permit AI use while setting strict review and data-handling expectations; it can also limit certain uses where it cannot verify the result or protect sensitive information.
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Maintainer work is not replaced simply because tools can draft or analyze code. OpenSSF’s summary of Linux Foundation maintainer-security research reports that 39% of surveyed maintainers and core contributors engaged in manual code review. That figure belongs to the report’s survey context, summarized by OpenSSF in 2024; it is not a new measurement of all maintainers in 2026.
The Linux Foundation’s 2025 global report describes gaps in governance and security frameworks and points to the need for formal governance, participation channels, and ongoing investment. Those needs sit alongside project-level rules: maintainers need time, documented contribution processes, security tools, and organizational backing to enforce any policy they adopt.
OpenSSF’s AI/ML Security initiative lists resources including a practical guide for maintainers and security engineers, OpenSSF Model Signing, and OSS-CRS, an orchestration framework for LLM-based bug-finding and bug-fixing systems. These illustrate work on security across the AI lifecycle, but the listing itself does not establish that any one resource is suitable for every project. A February 2026 Linux Foundation stakeholder discussion likewise recommends accountability and legal frameworks, standardized vocabulary and decisions, modernized security scaffolding, and support for open source communities (Open Source and the Future of AI).
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should a project decide where to draw its boundaries?
Start with the work the project must be able to trust, then match expectations to its ability to review. A small documentation-only repository, a widely deployed cryptographic library, and a project handling private vulnerability reports have different exposure and capacity. Each can state whether AI is acceptable for maintainers, contributors, or both, without treating those uses as equivalent.
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- Set submission expectations. Define disclosure, human responsibility, provenance, and the verification evidence required for different classes of change.
- Match review to risk and bandwidth. Choose checks that reviewers can actually perform, and be explicit about limits or delays rather than silently accepting an unreviewable queue.
- Revisit the policy as tooling changes. Evaluate whether the process protects the project and remains workable for contributors and maintainers; AI capabilities and security practices continue to evolve.
The practical question is not only whether AI-generated work is allowed. It is whether a project can preserve trust: contributors should understand the rules, maintainers should have a realistic way to verify submissions, and the surrounding ecosystem should invest in the security and governance that review depends on.
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