Official Updated 12/12/2025 02:17 PM

Preface: NVIDIA NeMo is a versatile, end-to-end platform used across a vast spectrum of industries, primarily to build, customize, and deploy generative AI agents and applications, such as custom chatbots and specialized assistants. Its primary users are companies in the computer software industry, but its use cases span many different sectors, including energy, telecommunications, financial services, healthcare, and retail.

NeMo is the framework for building models, while NIM (NVIDIA Inference Microservices) provides pre-packaged tools to easily deploy and manage those (and other) AI models as APIs, with NIM often using NeMo’s customized models for inference, creating a unified ecosystem.

Background: Uploading model checkpoints from the NVIDIA NeMo framework to NVIDIA NIM is essential for streamlining the transition from model development and training to optimized, scalable, production-ready inference. This integration merges the development power of NeMo with the robust, deployment-focused capabilities of NIM.

The process is more than a simple file transfer; it is a critical step in a comprehensive, end-to-end AI lifecycle management strategy. NVIDIA NeMo is a powerful framework for building, training, and fine-tuning large language models (LLMs) and other generative AI models, producing specialized .nemo checkpoints that contain model configurations and weights. NVIDIA NIM, or NVIDIA Inference Microservices, then takes these trained, domain-specific models and packages them into prebuilt, optimized, and secure microservices for deployment across various environments, whether in the cloud, on-premises data centers, or at the edge.

The restore_from() method is a function used for model loading and restoration. It allows you to: 

• Load a previously trained model instance, including its weights and configuration, from a saved .nemo file.
• Resume training from a specific point or perform fine-tuning/inference with a pre-trained model

Vulnerability details: CVE-2025-33226: NVIDIA NeMo Framework for all platforms contains a vulnerability where malicious data created by an attacker may cause a code injection. A successful exploit of this vulnerability may lead to code execution, escalation of privileges, information disclosure, and data tampering.

Official announcement: Please refer to the link for details – https://nvidia.custhelp.com/app/answers/detail/a_id/5736

Official Updated 12/12/2025 02:20 PM

Preface: Given Google’s efforts to make its hardware compatible with PyTorch, why is the NVIDIA Resiliency Extension (NVRx) still needed? The answer lies in the complementarity of these technologies, the realities of large-scale distributed systems, and the practical necessity to ensure training efficiency and fault tolerance, especially when using NVIDIA GPUs, which remain the mainstream platform for AI development. While Google is working to make its Tensor Processing Units (TPUs) more compatible with PyTorch to provide an alternative, the NVIDIA Resiliency Extension aims to address the specific and pressing challenges encountered when performing large-scale distributed PyTorch training on NVIDIA’s widely used hardware, such as hardware on cloud platforms like Google Cloud.

Background: The open-source machine learning framework PyTorch was originally developed by researchers at Facebook AI Research (now Meta AI). It was first publicly released in September 2016. PyTorch-based workloads are artificial intelligence (AI) and machine learning (ML) tasks and applications that are developed, trained, and run using the PyTorch open-source deep learning framework.

The NVIDIA Resiliency Extension (NVRx) integrates multiple resiliency-focused solutions for PyTorch-based workloads. Users can modularly integrate NVRx capabilities into their own infrastructure to maximize AI training productivity at scale. The NVIDIA Resiliency Extension (NVRx) is a specific Python package that integrates several solutions to minimize downtime and maximize the effective training time for PyTorch-based workloads running on NVIDIA infrastructure. NVRx is a Python package that framework developers and users can modularly integrate into their own infrastructure. It is used in major NVIDIA frameworks like the NVIDIA NeMo Framework for building large language models, providing the underlying machinery for their resilient training capabilities.

NVRx provides utilities that run the actual checkpoint saving routines in the background. It employs mechanisms, often leveraging torch.multiprocessing, to fork a separate, temporary process dedicated to handling the I/O operations after the data has been quickly staged from GPU memory to CPU buffers.

Vulnerability details: CVE-2025-33235 – NVIDIA Resiliency Extension for Linux contains a vulnerability in the checkpointing core, where an attacker may cause a race condition. A successful exploit of this vulnerability may lead to information disclosure, data tampering, denial of service, or escalation of privileges.

Official announcement: Please refer to the link for details – https://nvidia.custhelp.com/app/answers/detail/a_id/5746

Official Updated 12/12/2025 02:19 PM

Preface: The primary difficulties in robotics learning involve bridging the gap between simulation and reality (the “reality gap”), enabling robust perception and decision-making in unpredictable environments, handling complex physical interactions like contact dynamics, ensuring safety and security, addressing high costs and lack of standardization, and overcoming workforce skill gaps, all while managing computational and power limitations.

Background: Isaac Lab uses NVIDIA Isaac Sim’s capabilities for realistic virtual environments, allowing researchers to train complex robot behaviors efficiently through parallel simulation and data generation, then transfer these policies to physical robots. The primary difficulties Isaac Lab tackles are:

While the physical world remains the definitive testbed, the acquisition of physical interaction data with robots is expensive, time-consuming, and often necessitates specialized instrumentation. These limitations are especially acute in rare but safety-critical situations. Events such as high-speed collisions, hardware malfunctions, or navigation in unpredictable human environments are difficult to reproduce and pose significant risks to equipment and human safety.

NVIDIA Isaac Sim manages computational and power limitations through GPU-accelerated design, customizable performance settings, and scalable deployment options, allowing users to balance performance, fidelity, and resource consumption. This approach empowers developers to tailor the simulation environment to their specific hardware capabilities and project needs, from local workstations to cloud-based multi-GPU setups.

Key contributions of Isaac Lab

• Modular and scalable framework: Built on NVIDIA Omniverse, enabling high-fidelity, GPU-accelerated simulation for complex robots and tasks.

• Advanced sensor simulation: Supports tiled RTX rendering, Warp-based custom sensors, and physics-based data for rich observation spaces.

• Seamless teleoperation and data collection: Integrates spacemouse, VR headsets, and other devices for large-scale demonstration capture.

• Extensive environment suite: Provides diverse, ready-to-use environments for reinforcement learning, imitation learning, and sim-to-real research.

Vulnerability details: CVE-2025-32210 – NVIDIA Isaac Lab contains a deserialization vulnerability.  A successful exploit of this vulnerability might lead to code execution.

Official announcement: Please refer to the link for details – https://nvidia.custhelp.com/app/answers/detail/a_id/5733

Preface: Suppose you’re using cuML’s model persistence feature to load a serialized model from disk or a remote source. If the source is not trusted or validated, and the deserialization uses pickle or similar unsafe methods, it could execute arbitrary code.

The attached diagram demonstrates arbitrary code execution via pickle, which aligns with CVE-2025-33214 and likely CVE-2025-33213 if input validation is missing.

Background: NVTabular is a component of NVIDIA Merlin, an open source framework for building and deploying recommender systems and works with the other Merlin components including Merlin Models, HugeCTR and Merlin Systems to provide end-to-end acceleration of recommender systems on the GPU.

NVTabular requires Python version 3.7+. Additionally, GPU support requires:

• CUDA version 11.0+
• NVIDIA Pascal GPU or later (Compute Capability >=6.0)
• NVIDIA driver 450.80.02+
• Linux or WSL

When running NVTabular on the Criteo 1TB Click Logs Dataset using a single V100 32GB GPU, feature engineering and preprocessing was able to be completed in 13 minutes. Furthermore, when running NVTabular on a DGX-1 cluster with eight V100 GPUs, feature engineering and preprocessing was able to be completed within three minutes. Combined with HugeCTR, the dataset can be processed and a full model can be trained in only six minutes.

NVIDIA Merlin™ accelerates the entire pipeline, from ingesting and training to deploying GPU-accelerated recommender systems. Merlin NVTabular is a feature engineering and preprocessing library designed to effectively manipulate terabytes of recommender system datasets and significantly reduce data preparation time. It provides efficient feature transformations, preprocessing, and high-level abstraction that accelerates computation on GPUs using the RAPIDS™ cuDF library.

Vulnerability details:

CVE-2025-33214 – NVIDIA NVTabular for Linux contains a vulnerability in the Workflow component, where a user could cause a deserialization issue. A successful exploit of this vulnerability might lead to code execution, denial of service, information disclosure, and data tampering.

CVE-2025-33213 – NVIDIA Merlin Transformers4Rec for Linux contains a vulnerability in the Trainer component where a user may cause a deserialization issue. A successful exploit of this vulnerability may lead to code execution, denial of service, information disclosure, and data tampering.

Official announcement: Please refer to the following link for details-

https://nvidia.custhelp.com/app/answers/detail/a_id/5739