Hugging Face Partners with TruffleHog to Scan for Secrets
Hugging Face has announced a partnership with TruffleHog (TruffleSecurity) to integrate secret scanning into the Hugging Face platform. The integration aims to detect accidentally exposed credentials, API keys, and other secrets in model repositories and datasets. This addresses a growing security concern as the platform hosts an increasing volume of user-uploaded artifacts.
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Hugging Face and JFrog Partner to Improve AI Model Security Transparency
Hugging Face and JFrog have announced a partnership aimed at improving security transparency for AI models hosted on the Hugging Face platform. The collaboration likely involves integrating JFrog's software supply chain security capabilities with Hugging Face's model repository infrastructure. This addresses growing concerns about malicious or vulnerable models being distributed through open model hubs.
Hugging Face Teams Up with Protect AI: Enhancing Model Security for the ML Community
Hugging Face has announced a partnership with Protect AI to improve security for machine learning models hosted on the platform. The collaboration aims to address vulnerabilities in model files and supply chain risks that affect the broader ML community. Specific details about the technical implementation and scope of the security enhancements are not provided in the available content.
Hugging Face Partners with Wiz Research to Improve AI Security
Hugging Face has announced a security partnership with Wiz Research aimed at improving security practices across the AI model hosting platform. The collaboration focuses on identifying and addressing vulnerabilities in AI infrastructure and model supply chain security. This partnership reflects growing attention to security risks specific to AI platforms, including malicious model files and shared infrastructure threats.
Hugging Face and VirusTotal Collaborate to Strengthen AI Security
Hugging Face and VirusTotal have announced a collaboration aimed at improving security around AI models and artifacts hosted on the Hugging Face platform. The partnership likely involves integrating VirusTotal's malware and threat detection capabilities with Hugging Face's model repository ecosystem. This addresses growing concerns about malicious code or backdoors embedded in publicly shared model weights and datasets.
Hugging Face and AWS Partner to Make AI More Accessible
Hugging Face announced a strategic partnership with Amazon Web Services to expand access to AI models and tools. The collaboration aims to integrate Hugging Face's model hub and libraries more deeply with AWS infrastructure and services. This represents a significant enterprise deployment and cloud distribution move for the open-source AI ecosystem.
Hugging Face and Google Cloud Announce New Partnership
Hugging Face has announced a new partnership with Google Cloud, framed around building an open AI future. The blog post outlines collaboration between the two organizations, though the body content is not provided. This partnership likely involves deeper integration of Hugging Face's open-weights model hub and tooling with Google Cloud's infrastructure and services.
Hugging Face and Google Partner for Open AI Collaboration
Hugging Face and Google have announced a partnership focused on open AI collaboration, expanding access to Hugging Face models and tools on Google Cloud Platform. The deal deepens integration between Hugging Face's model hub and Google's cloud infrastructure, enabling easier deployment of open-source models via GCP services. This follows a pattern of major cloud providers forming strategic alliances with leading open-source AI platforms.
Announcing New Hugging Face and KerasHub Integration
Hugging Face and KerasHub have announced a new integration enabling users to access Hugging Face models and datasets directly through the Keras ecosystem. This partnership bridges two major ML frameworks, allowing Keras users to leverage the Hugging Face Hub's model repository without leaving the Keras workflow. The integration is aimed at reducing friction for practitioners who prefer Keras-based training and inference pipelines.