Endor Labs Helps Organizations Identify and Select Secure Open Source Artificial Intelligence Models

Endor Scores for AI Models ranks available options for security, popularity, quality and activity; in step forward for AI governance, developers can now start clean with AI models

Endor Labs, the leader in open source software security, today announced Endor Scores for AI Models, a unique capability that makes it easier than ever for companies to identify the most secure open source AI models currently available on Hugging Face, the popular platform that enables developers to share Large Language Models (LLMs), machine learning models and other open source AI models and dataset. As with Endor Labs' market-leading 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.

The AI landscape is the Wild West of technology development-it's unquestionably exciting and offers virtually unlimited potential for constant advancement. Also, while training each AI model to meet individual needs can be resource-intensive, platforms like Hugging Face feature a massive repository of possible options. However, convenience comes with complications: Those AI models could have exploitable vectors for attack, or rely on other models that can expose the network to even greater risks.

"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."

In many ways, this aspect of AI development mirrors the growth of open source software (OSS)-in both worlds, there's a wealth of options accompanied by often-hidden risks. In the case of OSS, every software package can bring dozens of indirect or 'transitive' dependencies, which is where most vulnerabilities reside. Similarly, Hugging Face offers a vast repository of open source, ready-made AI models, and developers focused on creating differentiated features can use the best of these to speed their own work. Again, there are serious risks involved, and Endor Scores for AI Models mitigates those by offering scores that enable developers to start with the most secure and appropriate options for their specific needs.

Those risks are both wide-ranging and dangerous. For example, pre-trained AI models from Hugging Face can harbor serious vulnerabilities, such as malicious code in files shipped with the model, or hidden within model 'weights.' When these models are integrated into an organization's infrastructure, they can bring major threats. There are also operational risks: These AI models can be modified based on other existing models, creating a complex dependency graph that can be hard to manage and secure.

There are also licensing obstacles-any failure to adhere to pre-set terms can spark legal headaches. Organizations need to be aware of intellectual property (IP) and copyright terms of all the AI models they use.

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.

Endor Labs
125 University Ave, Suite 88
Palo Alto, CA 94301 USA.

Paula Dunne
CONTOSDUNNECOMMUNICATIONS
1.408.776.1400

Endor Labs is an innovative startup that secures open source software (OSS), CI/CD pipelines and the supply chain - everything a company's code depends on. It gives DevSecOps teams the context they need to prioritize open source risk, secure CI/CD pipelines, and meet compliance objectives like Software Bills of Materials (SBOMs). It helps developers spend less time dealing with security issues and more time accelerating development through safe OSS adoption, and gives security leaders the visibility they currently don't have while utilizing third-party and open source software. The Endor Labs Platform gives organizations and government agencies the ability to maximize software reuse, and select, secure, and maintain OSS at scale. It secures everything a company's code depends on. The startup was named a finalist in both the RSA 2023 Innovation Sandbox, which highlights cybersecurity's boldest new innovators and Black Hat's Startup Spotlight Competition. It was named a Gartner Cool Vendor in Platform Engineering for Scaling Application Security Practices and one of the most innovative cybersecurity startups in Notable Capital's Rising in Cyber 2024.

This release was published on openPR.