AI Code Risks: Open Source & Copyright for Devs | IT-Medienrecht

AI Coding Tools: Navigating Copyright and Open Source License Risks

More and more developers, from startup founders to hobby programmers, are leveraging AI assistance tools such as GitHub Copilot, Amazon CodeWhisperer, or TabNine. These tools act as "AI pair programmers," suggesting lines of code or entire functions and promising a considerable boost in productivity. While these AI coding tools offer ingenious technical solutions, their legal implications raise significant questions.

Specifically, fundamental concerns arise regarding ownership of software generated by AI. Can third-party copyright or open source licenses inadvertently "slip into the code"? These license risks are increasingly coming into focus, especially for developers in Germany, making it a crucial topic that demands their attention.

Third-Party Code from AI: Copyright and the Copilot Debate

AI code generators like GitHub Copilot are trained on vast amounts of publicly available source code. A significant portion of this code is open source, often governed by strict licenses such as the GNU General Public License (GPL). For instance, Copilot was trained using GPL-licensed GitHub repositories.

This practice quickly drew criticism from the open source community. If Copilot suggests code originating from copyleft licenses, users might unknowingly violate these license terms. Some critics have even described this as "open source laundering," where third-party GPL code is integrated into seemingly license-free AI output.

The "Copyleft Surprise" Risk

The primary concern is the "copyleft surprise." An AI might suggest a code snippet that originally comes from a GPL library. If a developer accepts this proposal and integrates it into a proprietary product, the GPL license could obligate them to place the entire product under GPL. This scenario could be disastrous for a startup aiming to maintain commercial control over its software.

Critics warn that GitHub's Copilot institutionalizes this risk. Developers using Copilot for non-free software could inadvertently be forced into an unwanted open source license. In essence, AI-generated suggestions can subtly introduce license obligations without immediate detection.

US Lawsuit Against GitHub Copilot and Microsoft's Response

This issue is not merely theoretical. In the US, a class action lawsuit was filed by developers against GitHub, Microsoft, and OpenAI, the entities behind Copilot. The plaintiffs alleged that Copilot utilizes publicly licensed open source projects and generates code suggestions without adhering to original licenses, often omitting required license texts or copyright notices.

Microsoft and GitHub countered these claims, asserting that genuine 1-to-1 copies are rare. A California court, for example, dismissed some copyright allegations, as plaintiffs could not provide evidence of identical copies of their code produced by Copilot. GitHub also highlighted its integrated filter, designed to suppress suggestions too similar to known open source code.

However, the court allowed another aspect to proceed: potential violations of open source license terms. If Copilot reproduces code without citing the source, this could constitute a breach of the license agreement. This implies that even without direct copyright infringement, open source license conditions can still be violated, a legal nuance currently under judicial scrutiny.

Microsoft's "Copilot Copyright Commitment"

In response to these debates, Microsoft introduced a "Copilot Copyright Commitment" in the fall of 2023. This essentially offers indemnity against liability for paying Copilot customers. Microsoft pledged to indemnify developers against liability risks if third parties sued them for copyright infringements stemming from Copilot-generated code.

Specifically, if a Copilot user faces a copyright lawsuit due to Copilot's use or output, Microsoft committed to defending the customer and covering penalties or settlements. This promise, however, is contingent on users adhering to built-in filters and guidelines. The commitment primarily benefits commercial customers, such as those using Copilot for Business.

Critics view this as a marketing tactic, as Microsoft also clarified to authorities that users ultimately remain responsible when employing AI tools, much like operating a photocopier. For German developers, this means not blindly relying on a carte blanche. Despite Microsoft's potential defense, users can still face significant repercussions in the event of license violations.

Amazon CodeWhisperer & Co: Similar Problems, Different Approaches

While GitHub Copilot is arguably the most recognized AI coding tool, similar challenges confront other tools like Amazon CodeWhisperer, TabNine, and CodeGPT. Interestingly, Amazon CodeWhisperer has adopted a distinct approach to address these issues.

CodeWhisperer analyzes its suggestions and actively warns users if a snippet resembles open source training code. It then immediately provides the repository link and license information. This transparency allows developers to know, for instance, that a suggestion is under Apache 2.0, enabling an informed decision on its use.

Amazon states that it is the only AI coding assistant that displays such license references and can filter or flag risky suggestions. This "reference tracking" mechanism enhances awareness, highlighting that AI-generated code often originates from existing open source software. Other tools, like TabNine or AlphaCode, are also trained on public repositories, but their handling of licensing issues is less transparent.

Consequently, developers should critically evaluate all AI proposals. Always consider whether a suggested code section might originate from an existing project. When in doubt, it is always prudent to conduct a verification check.

Best Practices: Avoiding License Traps

How can developers protect themselves and their projects while still leveraging the benefits of AI coding assistants? Here are some best practices for individuals and teams:

• Check AI Output

  Do not treat AI suggestions as automatically license-free. For larger code blocks, perform a quick check, such as using a search engine to identify potential origins. Exercise caution if you find identical code in a well-known open source project.

• Utilize Copilot Filters

  GitHub offers a Copilot option that identifies and, if desired, blocks suggestions from public sources. Activating such filters can help prevent obvious license violations.

• Employ License Scanning Tools

  Integrate tools like Black Duck, FOSSA, or Snyk to scan your codebase for open source components. These tools often recognize copied snippets and identify associated licenses. Startups, in particular, should integrate such license compliance tools into their CI/CD process to prevent undeclared open source dependencies.

• Establish Internal Guidelines & Code Reviews

  Implement processes where pull requests and new code proposals are reviewed for license issues. Developers should be required to document the origin of external code, whether it’s from Stack Overflow, an open source library, or an AI assistant. Encourage team members to ask, "Did you write this code yourself?" especially for contributions from junior developers or freelancers.

• Maintain a Whitelist/Banlist for Licenses

  As a team, define which open source licenses are permissible (e.g., permissive licenses like MIT, Apache 2.0) and which should be avoided (e.g., GPLv3 for proprietary software due to its "infectious" copyleft nature). If AI suggests code from a GPL source, ensure everyone understands the implications and avoids its use unless prepared to open source their own code.

• Ensure Documentation and Attribution

  If open source code fragments are used, document them properly. Including a LICENSE file or a comment in the source code with the origin can later demonstrate compliance with license conditions, such as required author attribution or license text inclusion.

These measures, though requiring some initial effort, safeguard your project from unpleasant surprises. Discovering shortly before launch that a core component of your application is under GPL—because an AI tool "borrowed" it—can be a severe setback.

Publishing Code on GitHub: Public vs. Private

Beyond the challenges of AI code generators, managing your own code on platforms like GitHub presents another set of considerations. Many startups and hobby teams use GitHub for collaboration. But what are the legal implications of uploading code to GitHub?

Questions often arise: Are you permitted to simply upload code? What if you intend to use GitHub exclusively as an internal repository? Do you need contractual agreements to regulate whether others can fork or download your code?

Public vs. Private Repositories

GitHub offers both public and private repositories. If you make a project public, any GitHub user can, in principle, view, fork, and download your code. Forking is a fundamental GitHub feature, creating a copy of your repository in another account.

Legally, if you do not include a license file, your code remains protected by copyright, meaning "All Rights Reserved." However, others can still fork and view it on the platform, as GitHub's terms of use permit this. Without an explicit license, the permissions for third parties outside of GitHub to use the code remain unclear.

In such cases, third parties generally cannot simply use the code in their own projects without granted rights of use. This lack of clarity is undesirable for both the code owner and potential open source contributors.

Choosing a License for Public Repositories

Our tip: If you maintain a public GitHub repository, deliberately choose a suitable license. For open source code intended for reuse, permissive licenses like MIT or Apache 2.0 are excellent choices. These allow others to use, modify, and fork your code, even commercially, usually with only the obligation to mention or include the license text.

Conversely, if you wish for changes to also be disclosed, a copyleft license such as GPL is appropriate. With GPL, anyone can fork, but redistribution requires the same license. The key principle is: "no license, no clarity"—which can often lead to more harm than good. Familiarity with terms such as selecting an open source license, licensing code on GitHub, and forking repositories is essential for every founder.

Using GitHub for Private Collaboration

If you only intend to use GitHub privately, for example, as an internal code repository for your startup team, this is perfectly legitimate. Private repositories are only accessible to invited members, meaning no external party can fork or view the code.

In this scenario, an open source license is not required, as the code is not publicly published. Nevertheless, clear internal rules are vital regarding access and confidentiality, often supported by internal Non-Disclosure Agreements (NDAs). GitHub's terms of use also apply to private repos, but as long as no illegal content is uploaded, it functions effectively as a remote Git server without automatically assigning rights to third parties. Always be vigilant to avoid accidentally pushing secret keys or sensitive data publicly, a common security risk for unwary teams.

Regulating Forks and Usage

Regarding the regulation of forks: if your code is public and open source, the chosen license already defines what others are permitted to do. Forks are a constant feature on GitHub; a license like MIT or GPL formally grants permission. If you do not want others to use your code, you should not make it public. A "public, but please don't touch" approach is neither practical nor legally robust, as public repositories inherently attract interest.

License Agreements within the Team and with Freelancers

Startups frequently engage external developers or freelancers. When drafting contracts, special attention is required to prevent future complications. Key contractual points include:

• Clear Transfer of Rights

  The developer or freelancer contract must explicitly state that all copyrights and rights of use for the created software are transferred to the company. In Germany, while the author (programmer) retains inalienable moral rights, exclusive rights of use can be fully transferred by contract. This ensures the company can use, modify, and publish the code—for example, on GitHub—without needing separate permission from the developer. If you intend to open source parts of the code, the contract should cover this, for instance, by including a clause that entitles the client to publish the code under an open source license.

• Regulation of Open Source Use

  Establish clear agreements with employees and freelancers regarding which open source components may be used. The contract can stipulate that no copyleft-licensed software (e.g., GPL) may be integrated without prior consent. This prevents unnoticed integration of a GPL library that could "infect" your entire product's licensing. Only permit open source components with compatible licenses (e.g., MIT, Apache) and require a comprehensive list of all open source dependencies.

• Warranty and Indemnification

  Ensure that the delivered code is free of third-party rights, meaning it has not been copied and does not violate existing licenses. Many freelancer contracts include a guarantee that they will only deliver original code or declare all third-party code used. Should a hidden license violation occur—for example, a developer copying Stack Overflow code under CC-BY-SA without declaration—the contract should include an exemption from liability in favor of the startup. The developer would then be liable for any resulting damages or claims.

• Proactive Communication

  Some freelancers may not be fully aware of complex licensing issues. Therefore, address this topic early in discussions. If your startup plans to publish the created code on GitHub later, whether as an open source project or a visible portfolio, communicate this clearly. Transparency fosters acceptance among developers regarding publishing their code under a specific license, even if contracts generally allow such publications.

• Handling Apache/MIT Licenses

  If a developer uses Apache 2.0 licensed snippets, remember that while Apache 2.0 is a permissive license, it requires maintaining license and copyright notices in the source code or documentation. Ensure all required NOTICE or LICENSE files are present if Apache code is integrated. Similar, though often simpler, requirements apply to MIT licenses, which typically require a short license text with attribution. These formal legal obligations should be explicitly mentioned in the contract or covered by internal policies.

• Confidentiality and Internal GitHub Usage

  If a freelancer works on an internal GitHub repository, this arrangement should be covered by a Non-Disclosure Agreement (NDA). Code intended to remain private must be treated as confidential. The freelancer should commit not to place any of this code in public repositories without authorization or share it otherwise. This is particularly crucial if only parts of the code are intended for future open-source publication, ensuring the rest remains internal.

In summary, robust contracts guarantee your startup retains full control over its code and its licensing. When in doubt, seek legal review of developer contracts, especially if you plan to publish code or need to circumvent strictly regulated open source licenses.

Conclusion: Using Nerdy Tools with Responsibility

AI code tools like GitHub Copilot are captivating assistants that streamline development, offering time-saving autocomplete magic. However, a legal perspective warns that unawareness of open source license risks can quickly negate these benefits. Particularly in Germany, with its stringent copyright principles, it is crucial not to assume that AI output is inherently unproblematic.

For startups, game development teams, and hobby projects, the message is clear: utilize new AI assistants, but maintain vigilance. Thoroughly check larger code snippets, implement robust tools and policies for license compliance, and continuously train your team. When sharing code on GitHub, select an appropriate license or keep the repository private, aligning with your project goals.

Furthermore, ensure that all contributors are contractually aligned, as license violations due to ignorance can have long-term repercussions. Ultimately, technology should be an enabler, not a source of legal impediments. By applying foresight and adhering to the best practices outlined, you can fully enjoy the advantages of AI code tools without the hidden dangers of open source licenses. No one wants to face the prospect of shutting down a promising startup simply because GPL code unknowingly found its way into their project. Therefore: happy coding with clean code!