Understanding GitHub Copilot

GitHub Copilot functions as an AI pair programmer, powered by a large language model developed by GitHub and OpenAI. It is trained on a massive dataset of publicly available code. Its primary purpose is to suggest code, functions, and even entire blocks of code as a developer writes, integrating directly into various code editors. It predicts and offers code completions based on context, comments, and surrounding code.

Potential Safety Concerns

While designed to boost productivity, the use of AI code generation tools like GitHub Copilot introduces several potential safety and security considerations that developers and organizations should be aware of.

Data Privacy and Code Confidentiality

A significant concern involves the potential for developers' private code to be used or exposed. The AI processes the code being written to generate suggestions. There are questions about how this data is handled, whether it's stored, and if it could inadvertently influence suggestions for other users or be accessible in any way that compromises confidentiality, especially when working on proprietary or sensitive projects.

Licensing and Copyright Implications

Since Copilot is trained on a vast amount of publicly available code, there is a risk it might suggest code snippets that are copied verbatim or nearly verbatim from the training data. This raises questions about potential licensing violations if the suggested code originates from repositories with restrictive licenses (e.g., GPL) and is then incorporated into a project with an incompatible license (e.g., a proprietary project). Identifying the origin and license of every suggested snippet is practically impossible for developers.

Code Quality and Security Vulnerabilities

AI models learn from the data they are trained on. If the training data contains insecure or poor-quality code, Copilot might suggest similar patterns. This could potentially introduce security vulnerabilities (like injection flaws, weak cryptography, or insecure defaults) or technical debt into projects if suggestions are accepted without careful review and understanding. The suggested code is not guaranteed to be secure, optimal, or free from bugs.

Over-Reliance and Skill Degradation

Although not a direct security flaw of the tool itself, a potential safety concern is the risk of developers becoming overly reliant on AI suggestions. This could lead to a decreased understanding of the underlying code, algorithms, or security principles, potentially hindering their ability to identify and fix issues manually or write secure code independently in the future.

How GitHub and Microsoft Address Safety

GitHub and Microsoft have implemented measures and provided guidance to address some of these concerns:

• Data Handling: According to GitHub's policies, private code, prompts, and suggestions are handled according to their privacy statement. For business users, there are options to prevent the transmission of code snippets back to GitHub or Microsoft, ensuring that the code context used for suggestions remains within the organization's control and is not used to train future models.
• Filtering Suggestions: Efforts are made to filter out suggestions that are direct copies of public code longer than a specific threshold (e.g., 150 characters). While this helps reduce verbatim replication, it doesn't eliminate the possibility of shorter snippets or highly similar code appearing.
• Security Training: The models are continuously being refined, with efforts to reduce the likelihood of generating insecure code patterns, though this remains an ongoing challenge in AI development.
• Guidance and Best Practices: GitHub provides documentation encouraging users to treat Copilot suggestions like any other code written by a colleague – requiring review, testing, and verification.

Best Practices for Using Copilot Safely

Using GitHub Copilot effectively and safely requires diligence and integration into existing development workflows.

• Code Review Practices: Suggestions from Copilot should be subject to the same rigorous code review process as code written manually. Peer review helps catch potential bugs, security vulnerabilities, and licensing issues that Copilot might introduce.
• Handling Sensitive Information: Avoid using Copilot when working with highly sensitive or regulated code if organizational policies or compliance requirements prohibit transmitting any code context to external services, even with privacy features enabled. Understand the data handling policies associated with the specific Copilot plan being used (Individual vs. Business).
• Licensing Verification: While challenging, developers should be aware of the licenses of dependencies and major code sources used in their projects. If Copilot suggests a significant block of code that seems generic but critical, a quick search or understanding of common patterns in the relevant domain can sometimes help identify potential origins. Ultimately, the responsibility for code licensing compliance rests with the developer and organization.
• Testing and Validation: Suggested code must be thoroughly tested. Unit tests, integration tests, and security scans (like SAST and DAST tools) are crucial for identifying functional issues or vulnerabilities that Copilot's suggestions might contain. Do not assume suggested code is correct or secure.
• Understanding Suggestions: Developers should not blindly accept suggestions. Taking the time to understand why Copilot is suggesting a particular piece of code, how it works, and its implications for the project's architecture, performance, and security is vital. Use Copilot as an assistant, not a replacement for fundamental coding knowledge and critical thinking.

Conclusion: Assessing Copilot's Safety

GitHub Copilot is a powerful tool that can significantly improve developer productivity. However, labeling it as unequivocally "safe" or "unsafe" is an oversimplification. Its safety depends heavily on how it is used and the safeguards implemented by the developer and organization.

Potential risks related to data privacy, licensing, and code quality exist. GitHub and Microsoft have implemented measures to mitigate these, particularly concerning data handling for business users and filtering verbatim code.

Ultimately, responsible use requires treating Copilot's suggestions as external contributions that need verification, testing, and careful review, just like code from any other source. By integrating Copilot into established secure development lifecycles and following best practices, organizations can leverage its benefits while managing the associated risks. The tool is safe to use when used safely.