Endor Labs, the open source software security company, has announced Endor Scores for AI Models, a new feature designed to help companies easily identify the most secure open source AI models available on Hugging Face. Hugging Face is a widely-used platform where developers share Large Language Models (LLMs), machine learning models, and other open source AI models and datasets.

As with Endor Labs’ release, DroidGPT—an AI that helps developers select better open source software —Endor Scores for AI Models uses 50 out-of-the-box metrics to score all available AI models for security, popularity, quality and activity. The release represents a major step forward in AI governance by enabling developers to start clean with AI models, a goal that has so far proved elusive.

“It’s always been our mission to secure everything your code depends on, and AI models are the next great frontier in that critical task,” said Varun Badhwar, co-founder and CEO of Endor Labs. “Every organization is experimenting with AI models, whether to power particular applications or build entire AI-based businesses. Security has to keep pace, and there’s a rare opportunity here to start clean, and avoid risks and high maintenance costs down the road.”

As the company puts it, Endor Scores for AI Models simplifies the task of identifying the best and most secure AI models from the array of options available on Hugging Face. Developers don’t even need to know the names of particular models—the process of finding the best options can start with questions such as: What models can I use to classify sentiments? What are the most popular models from Meta? And what is a popular model for voice in Hugging Face? In response, Endor Scores for AI Models will offer crisp scores that rank both the positive and negative aspects of each model. Developers can then select the most appropriate models for their particular needs.

Endor Scores for AI Models is available now for existing customers. Get your free 30-day trial here and try it for yourself.