Addressing the Challenges of Ensuring Open Source License Compliance and Minimizing Security Risks with the Use of AI-Generated Code

Introduction Generative AI technologies have revolutionized software development, providing developers with automated assistance to help developers in writing code more…


Generative AI technologies have revolutionized software development, providing developers with automated assistance to help developers in writing code more efficiently and effectively. Generative AI technologies have become indispensable tools for software engineers, enhancing productivity and innovation. These systems generate code snippets aligned with desired functionality by analyzing existing code repositories. They automate repetitive tasks, accelerate development cycles, and foster the exploration of alternative and more innovative coding approaches. Generative AI leverages vast amounts of open source code to provide valuable insights, assisting developers in creating high-quality software efficiently.

However, as these systems are trained on publicly available open source code, concerns arise regarding open source license compliance and the security implications of verbatim code copying. In this blog post, we will explore the importance of snippet discovery for compliance with open source licenses and to identify vulnerable source code that may have been copied by the AI system from open source code.

Open Source Training Data

Generative AI models are typically trained on publicly available open source code. This training data enables the AI system to learn coding patterns, structures, and styles. However, it is essential to note that the generated code may include verbatim code copied from the open source repositories. This raises concerns regarding open source license compliance and the security implications of incorporating vulnerable code into the AI-generated output. There are three specific use cases that we would like to explore in this blog post:

Use Case 1: 100% AI-Generated Code

In scenarios where AI-generated code does not match any existing open source code, there are no open source license obligations to fulfill. Since the code is entirely original and created by the AI system, companies can freely use and distribute it without restrictions imposed by open source licenses. This scenario offers flexibility and freedom to utilize AI-generated code for various purposes without the need for extensive license compliance or even security vulnerability analysis.

Use Case 2: Mixed Open Source and AI-Generated Code

When AI-generated code includes open source snippets in its output, companies must ensure compliance with the associated open source licenses. However, the critical question is: How do we know if the AI system has provided copied open source code as part of its output? This scenario involves identifying the specific open source code snippets within the generated code, understanding the terms of the relevant licenses, and fulfilling the obligations accordingly. Companies need to consider factors such as proper attribution, license compatibility, and ensuring the availability of the corresponding open source code and modifications. In addition, companies need to ensure that the source code copied from open source origins does not contain any vulnerable code. Hence, an SCA tool is needed to identify source code snippets, including their origin and license.

Use Case 3: 100% Open Source Code

In this scenario, the AI system simply provided a snippet of code copied at 100% from an open source repository. This clear case demonstrates the need for snippet support in the SCA tool to identify open source code, its origin, and applicable license.

Compliance with Open Source Licenses

When AI-generated code includes open source snippets, companies must fulfill the license obligations associated with those snippets. This involves identifying the specific open source code used, understanding the license terms, complying with obligations such as attribution source code distribution, and ensuring no license compatibility issue with other source code. Compliance ensures that open source contributors’ rights are respected, contributing to a healthy and collaborative software ecosystem. However, the challenge is that such AI systems do not flag if any source code they provide comes from an existing open source project. In this case, identifying open source code falls on the user receiving the source code from the AI system.

Security Implications and Snippet Discovery

In addition to license compliance, snippet discovery holds immense importance from a security perspective. Verbatim copying of vulnerable code from open source projects by the AI system can introduce security risks into the generated code. Snippet discovery helps identify such vulnerabilities, allowing developers to take appropriate measures to remediate or mitigate them. By leveraging software security analysis tools during snippet discovery, companies can proactively identify and address security weaknesses, minimizing the potential impact on the final software product.

The Role of Software Composition Analysis Tools

Software Composition Analysis (SCA) tools are vital in snippet discovery for license compliance and security. These tools analyze the AI-generated codebase, comparing it against known open source repositories and vulnerability databases. By identifying verbatim code matches and cross-referencing them with vulnerability data, SCA tools enable developers to identify potential security vulnerabilities derived from copied code. This empowers developers to address these vulnerabilities and ensure the overall security of the software.


As generative AI technologies assist developers in code generation, snippet discovery holds significant importance from license compliance and security perspectives. Companies must address the challenges of open source license compliance and mitigate security risks from verbatim copying of vulnerable code. By utilizing SCA tools that offer snippet discovery and identification functionalities, developers can accurately detect open source code snippets in AI-generated code, fulfill license obligations, and proactively identify and address security vulnerabilities. This approach fosters a responsible open source usage culture, ensuring software products are compliant and secure.

At FossID, since our inception, we’ve been proud to have been offering our clients the ability to discover open source snippets in source code bases, identify their original components, and license and report any found security vulnerabilities. We’re happy to extend your organization a time-limited license to test drive and compare our tool to whatever other tool(s) you use. We are confident that you will be pleased with what we offer.

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