Tabnine's new Provenance and Attribution capability supports the use of large language models by checking AI-generated code for licensing restrictions. Credit: Shutterstock/TippaPatt Looking to minimize IP liability for generative AI output, Tabnine’s AI coding assistant now checks code for licensing restrictions. The Code Provenance and Attribution capability added to the tool enables enterprise developers to use large language models (LLMs) while minimizing the possibility of restrictively licensed code being injected into a codebase. With this new feature, Tabnine will more easily support development teams and their legal and compliance teams who wish to leverage a variety of models, the company said. Now in private preview, the Provenance and Attribution capability was announced on December 17. Tabnine now can check code generated using AI chat or AI agents against code publicly visible on GitHub. It then flags any matches and references the source repository and its license type. This detail makes it easier for engineering teams to review code being generated with the assistance of AI and decide if the license of that code meets specific requirements and standards, Tabnine said. Models trained on larger pools of data outside of permissively licensed open source code can provide superior performance, but enterprises using them run the risk of running afoul of IP and copyright violations, Tabnine president Peter Guagenti said. The Code Provenance and Attribution capability addresses this tradeoff and increases productivity while not sacrificing compliance, according to Guagenti. And, with copyright law for using AI-generated content still unsettled, Tabnine’s proactive stance aims to reduce the risk of IP infringement when enterprises use models such as Anthropic’s Claude, OpenAI’s GPT-4o, and Cohere’s Command R+ for software development. The Code Provenance and Attribution capability supports software development activities including code generation, code fixing, generating test cases, and implementing Jira issues. Future plans include allowing users to identify specific repositories, such as those maintained by competitors, for generated code checks. Tabnine also plans to add a censorship capability to allow administrators to remove matching code before it is displayed to the developer. Generative AISoftware DevelopmentOpen Source