Businesses embracing AI-assisted coding are unknowingly exposing themselves to open-source license violations and intellectual property risks, a new legal analysis from U.K. law firm Trethowans warns. The analysis, authored by technology lawyer Laura Trapnell, highlights that engineers using tools like GitHub Copilot, Cursor, and ChatGPT may be generating 'shadow code'—internal software that incorporates open-source components without proper licensing compliance.
What the Trethowans Analysis Found
The warning centers on a practice Trapnell calls 'vibe coding,' where developers lean heavily on AI suggestions without fully reviewing the provenance of the generated code. According to Trapnell’s analysis, AI coding assistants are trained on vast repositories of open-source code, and they often reproduce snippets verbatim or with slight modifications. Engineers who use these assistants without rigorous review may unwittingly incorporate code governed by copyleft licenses—such as the GPL—into proprietary software. That mistake can potentially force a company to open-source its entire codebase or face legal action from copyright holders.
The analysis stresses that the risk is not hypothetical. Numerous lawsuits, including a class-action suit against GitHub and OpenAI, have alleged that AI models trained on public code are infringing copyrights. While those cases are still winding through courts, Trapnell notes that companies cannot afford to wait for a definitive ruling. The immediate danger is internal: development teams, lulled by the speed of AI autocomplete, are pushing code into production that carries hidden licensing obligations.
Who Is Affected and How
The impact of vibe coding cascades across an organization.
For developers: The primary challenge is a loss of transparency. When an AI assistant suggests a block of code, it rarely cites a source. A developer might accept a function that does exactly what they need, ignorant that it was lifted from a library with a restrictive license. Even permissive licenses like MIT or Apache 2.0 require attribution, which is often overlooked in the rush to ship features.
For IT managers and DevOps leads: The rise of shadow code mirrors the earlier phenomenon of shadow IT, where employees deployed unsanctioned cloud services. Here, the unsanctioned element is the provenance of the code itself. Traditional software composition analysis (SCA) tools may not flag AI-generated code as third-party, because it enters the codebase as an original creation. Managers must now extend their supply chain governance to include AI-assisted development.
For legal and compliance teams: The stakes are high. Violating a copyleft license can strip a company of its proprietary rights over derivative works. In the event of a merger, acquisition, or public offering, unexpected open-source entanglements can delay or derail deals. Trapnell’s analysis underscores that standard IP diligence questionnaires are now incomplete if they don’t probe AI coding practices.
For business owners and executives: The allure of AI-driven productivity gains is real—some organizations report 30–50% faster coding cycles. But the hidden liability can swamp those gains if a single compliance lapse triggers litigation or forces a product rewrite. The analysis warns that directors could be held personally accountable if they fail to oversee proper risk management.
How AI Coding Created This Compliance Minefield
The roots of the problem lie in the data pipelines that feed AI coding tools. When GitHub Copilot launched in 2021, it was trained on billions of lines of public code hosted on GitHub, irrespective of the licenses attached to those repositories. OpenAI’s Codex, the engine behind Copilot, learned patterns, structures, and even whole functions from that corpus. Subsequent tools like Amazon CodeWhisperer and Google’s Duet AI adopted similar methodologies.
Critics immediately pointed out that training on copyleft code could result in the model reproducing protected expressions. In November 2022, a group of developers backed by the Joseph Saveri Law Firm filed a class-action lawsuit alleging copyright infringement and breach of open-source licenses. That case, Doe v. GitHub, remains unresolved. Meanwhile, the Linux Foundation and others have called for clearer guidelines on AI and copyright.
What makes the current risk acute is the culture shift among developers. Tool integration into IDEs like Visual Studio Code means AI is no longer a separate step—it’s part of every keystroke. Developers can generate hundreds of lines in seconds. The term 'vibe coding'—coined recently in developer communities—captures a mindset where the programmer acts less as a crafter and more as a curator of AI output. Speed trumps scrutiny. Even when licenses are respected, the absence of attribution breaks the social contract of open source, potentially discouraging community contributions over time.
What You Should Do Right Now
The Trethowans analysis offers a practical roadmap for companies to regain control. Here are immediate steps drawn from Trapnell’s recommendations and industry best practices.
1. Update your software development policies
Explicitly require that any AI-generated code be reviewed for license compliance before merging. Define acceptable use: some organizations may forbid AI-generated code in sensitive modules altogether. Include AI tools in the scope of your acceptable use policy.
2. Deploy AI-aware scanning tools
Traditional SCA tools like Black Duck, Snyk, or FOSSA already check dependencies for known licenses. But you may need to extend your pipeline to scan code snippets for matches to open-source databases. Some vendors, including FOSSA and Snyk, are adding AI-specific scanning features that flag code with high similarity to public repositories.
3. Train your developers
Make sure every engineer understands the basics of open-source licensing: copyleft vs. permissive, attribution requirements, and the consequences of non-compliance. A one-hour workshop can pay for itself many times over.
4. Leverage vendor indemnity where available
Microsoft offers Copilot customers an indemnification policy that covers certain copyright claims, provided the customer uses content filters and tags AI-generated code. Review these terms carefully—they may not cover all scenarios, and they usually exclude custom AI models or third-party tools like Cursor.
5. Keep an inventory of AI-generated code
Consider requiring a simple comment tag (e.g., // AI-generated, see prompt X) for any code that originated from an AI suggestion. This creates an audit trail and helps compliance teams focus reviews.
6. Stay informed about legal developments
The Doe v. GitHub case could set a precedent on whether AI training on open-source code is fair use. Regulatory bodies in the EU and the U.S. are also eyeing AI accountability. Assign someone on your legal or compliance team to track these shifts.
Outlook
AI-assisted coding is not a fad—it is becoming the default. Gartner predicts that by 2028, three-quarters of enterprise software engineers will use AI code assistants, up from fewer than one in ten in early 2023. As the technology matures, model providers are experimenting with attribution mechanisms. For instance, Google’s Duet AI can optionally cite matching repositories in its suggestions. Microsoft has also indicated it is working on provenance features for Copilot.
Yet the fundamental tension remains: the speed and convenience of AI generation incentivize developers to skip verification. Until liability laws catch up, the burden falls squarely on the organizations that employ these tools. The Trethowans analysis makes clear that the companies most at risk are those that treat AI like a magic black box. Those that invest early in governance will not only avoid courtroom dramas but also build more trustworthy software. As Trapnell warns, 'Vibe coding might feel productive until the first cease-and-desist letter arrives.' The time to act is before that moment.