In-Short
- Endor Labs introduces ‘Endor Scores for AI Models’ to evaluate AI security and quality.
- The scoring system is designed for Hugging Face’s open-source AI models and datasets.
- Endor Labs aims to improve AI governance and mitigate risks in AI model integration.
Summary of Endor Labs’ AI Model Scoring Tool
Endor Labs has launched a new evaluation system called ‘Endor Scores for AI Models,’ which assesses AI models based on security, popularity, quality, and activity. This tool is particularly aimed at models shared on Hugging Face, a platform for Large Language Models (LLMs), machine learning models, and datasets. As the adoption of ready-made AI models grows, Endor Labs’ scoring system seeks to enhance AI governance and help developers choose secure and high-quality AI models.
Varun Badhwar, the CEO of Endor Labs, emphasizes the importance of securing AI models as they become integral to applications and businesses. The company’s approach views AI models as dependencies in the software supply chain, necessitating rigorous security and risk assessments similar to other open-source components.
The tool performs 50 checks on AI models, generating an ‘Endor Score’ that reflects various factors, including maintenance, sponsorship, release frequency, and known vulnerabilities. Positive scoring factors include safe weight formats and licensing information, while negatives include incomplete documentation and unsafe weight formats.
Endor Labs’ scoring system is user-friendly, allowing developers to search for models based on general queries rather than specific model names. This feature aids in the selection of suitable models for developers’ needs, ensuring both functionality and safety.
Image credit: Screenshot of Endor Labs’ tool for scoring AI models (Photo by Element5 Digital on Unsplash).
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