What Is an Internal Fork?
Software today is increasingly composed of code and components from diverse origins. Integrating third-party software into your own projects can be approached in several ways. Ideally, software components are integrated as libraries or modules managed by package managers. This “managed” or “declared” approach allows for streamlined updates, effective vulnerability management, and a clear separation between proprietary and external code.
However, reality often deviates from the ideal scenario. Developers may for example incorporate third-party code directly at the source code level, either by copying entire components or selectively copying fragments. This practice creates what is commonly known as an “internal fork”—a self-managed, independent version of an external software component. It is important to clarify that internal forks can occur both intentionally, such as consciously maintaining a customized version of an open-source project internally, and unintentionally, where minor modifications gradually lead to a divergent version without explicit intention or awareness.
Why are Internal Forks a Problem?
Unintentional internal forks are particularly problematic. A common scenario occurs when developers initially integrate approved open-source components without the intent to modify them. However, as compatibility issues arise, minor changes are introduced to resolve these issues. These incremental modifications might seem insignificant individually, yet cumulatively they transform an approved, managed component into an unmanaged internal fork—often unnoticed by development teams.
Internal forks introduce considerable risks and complications. Primarily, there’s no structured framework in place to maintain or update these copied code segments. Even if the original software was initially integrated as managed code, once modifications occur, it becomes effectively unmanaged. Teams may no longer have explicit tracking or monitoring mechanisms, increasing the risk of overlooking vulnerabilities discovered in the original component. Moreover, manual modifications inevitably cause divergence from the original source, complicating or even preventing the adoption of future patches and enhancements. This divergence not only complicates maintenance but amplifies security vulnerabilities and license compliance risks.
How Does AI Impact Internal Forking?
The rise of generative AI has made this challenge even bigger. Generative AI represents an extraordinary advancement in code generation and developer productivity. Its capabilities have transformed coding practices by popularizing a new paradigm for code integration. Previously, developers predominantly integrated third-party code at the component or library level, leveraging package managers to handle updates and dependency management. Now, generative AI frequently generates code as individual files or snippets, prompting developers to integrate code at a much more granular, source-code level.
The shift from component-level integration (where I for example leverage a library) to source-level integration (where I directly include files or snippets of source code) increases the likelihood of internal forks. It’s partly a matter of mindset—developers are becoming more open to the idea of integrating fragments of external code that they did not write themselves. But it’s also about the lowering of practical barriers: Before generative AI, modifying large external codebases was complex and served as a deterrent. Creating an internal fork could require time and effort. However, today, modifications to a 3rd party code base are merely an AI prompt away, dramatically increasing the risk of an internal fork.
Internal Forks Accumulate Technical Debt
Internal forks often remain hidden, becoming apparent only when a security incident, license compliance problem, or maintenance crisis emerges. Basic Software Composition Analysis (SCA) tools, primarily designed for declared or managed software components, often fail to detect these internal forks since they may not track unmanaged or modified source code effectively. Consequently, internal forks become invisible, silently accumulating significant future risks and costs.
From a business perspective, internal forks pose substantial challenges. They introduce hidden costs related to maintenance burdens, security vulnerabilities, and license compliance issues. For mergers and acquisitions, clearly identifying the presence of internal forks within a codebase or identifying snippets or modifications of open-source software is crucial for accurate risk and value assessment. Even outside M&A contexts, proactively addressing internal forks is essential to minimize future burdens.
Embrace AI Coding Responsibly
Fortunately, addressing this issue, although challenging, is entirely achievable. Awareness is the first step – recognizing internal forks as problematic and prevalent is crucial. Organizations should adopt modern SCA tools capable of identifying code at the source-file or snippet level, ensuring visibility even for modified or unmanaged code. These advanced tools can uncover hidden internal forks, providing visibility and enabling proactive management.
Implementing rigorous internal processes and workflows, supported by advanced tooling, significantly mitigates risks associated with internal forks. Automated detection of internal forks integrated at the source-code level should become central to modern development practices. While generative AI undoubtedly revolutionizes software development, embracing it responsibly requires robust safety nets. By proactively managing internal fork risks, organizations can fully harness AI’s transformative potential without incurring hidden, costly technical debt, security vulnerabilities, or compliance risks.
In conclusion, generative AI represents an extraordinary opportunity for the software industry. However, it necessitates a shift in managing external code integration. With the right tools and processes in place, organizations can safely leverage AI’s potential while effectively managing the growing challenge of internal forks.
