Official Updated 11/26/2025
Preface: AI vision models are artificial intelligence systems, often multimodal (Vision-Language Models or VLMs), that analyze and interpret visual data (images, videos) alongside text, enabling them to “see” and understand the world like humans, performing tasks from object recognition and image captioning to answering questions about visuals and generating new images, by converting visual info into a format comparable to text.
Background: You use NVIDIA TAO (Train, Adapt, Optimize) to rapidly build, customize, and deploy high-performance, domain-specific AI models (especially for vision) with less code, less data, and faster training by leveraging powerful pre-trained foundation models, fine-tuning them with your own data, and optimizing them for efficient inference on edge-to-cloud devices, saving significant time and resources.
The NVIDIA TAO Toolkit is designed to function with both real and synthetic data.
Training with Real Data: The primary function of the TAO Toolkit is to fine-tune NVIDIA’s extensive library of pretrained foundation models using your own proprietary (real-world) datasets. This process is low-code and enables the customization of models for specific use cases without needing deep AI expertise or training from scratch.
Leveraging Synthetic Data: Synthetic data is often used to address the challenges associated with real data collection, such as scarcity, expensive labeling, and rare edge cases.
Models can be initially trained on large volumes of synthetic data generated from tools like NVIDIA Omniverse Replicator or partner platforms (e.g., Sky Engine AI, AI. Reverie, Lexset).
Vulnerability details: (CVE-2025-33208) NVIDIA TAO contains a vulnerability where an attacker may cause a resource to be loaded via an uncontrolled search path. A successful exploit of this vulnerability may lead to escalation of privileges, data tampering, denial of service, information disclosure
Official announcement: Please refer to the link for more details.