Official Updated 08/11/2025 06:15 AM

Preface: WebDataset is a PyTorch IterableDataset implementation designed for efficient access to large datasets stored in POSIX tar archives. It focuses on sequential/streaming data access, which offers substantial performance advantages in environments where local storage is limited or I/O bottlenecks are a concern. WebDataset is particularly well-suited for very large-scale training, as it minimizes the need for local storage and allows for efficient data loading from various sources, including cloud storage.

Background: NVIDIA WebDataset refers to the integration of WebDataset with NVIDIA technologies like DALI or NeMo, rather than a separate NVIDIA-specific installation. Installing WebDataset itself is straightforward, as it is a Python library.

• DALI is a portable, open-source software library for decoding and augmenting images, videos, and speech to accelerate deep learning applications.

DALI itself doesn’t extract .tar files directly — instead, it processes data streamed from tarballs via WebDataset or other loaders.

• NVIDIA NeMo is a framework for building and deploying generative AI models, particularly those used in conversational AI like speech recognition and natural language processing.

It may extract or stream data depending on the configuration, but tarball handling is abstracted behind the data pipeline.

Vulnerability details: CVE-2025-23294 – NVIDIA WebDataset for all platforms contains a vulnerability where an attacker could execute arbitrary code with elevated permissions. A successful exploit of this vulnerability might lead to escalation of privileges, data tampering, information disclosure, and denial of service.

Official announcement: Please see the link for details

https://nvidia.custhelp.com/app/answers/detail/a_id/5658

Preface: Deep learning in AI generally learns much faster than humans in specific, narrow tasks, especially those involving large datasets and complex computations. However, humans still excel at general intelligence, creative problem-solving, and learning with limited data.

Perhaps, AI does not have this advantage yet!

Background: Keras 3.0 is a major rewrite of the Keras deep learning API, designed to provide a unified and flexible platform for building and deploying deep learning models. Its most significant feature is its multi-backend architecture, allowing users to run Keras workflows on top of various popular deep learning frameworks.

TensorFlow is a comprehensive, low-level machine learning framework capable of building and training models directly. However, Keras plays a crucial role as its official high-level API, providing several benefits that make deep learning development significantly easier and more efficient within the TensorFlow ecosystem.

Keras 3.0 does it work in lambda layer? Yes, the Lambda layer continues to be available and functional in Keras 3.0. In machine learning, specifically within the context of deep learning frameworks like Keras or TensorFlow, a Lambda layer is a type of layer that allows you to wrap arbitrary expressions or functions as a layer in your neural network model.

Vulnerability details: A safe mode bypass vulnerability in the `Model.load_model` method in Keras versions 3.0.0 through 3.10.0 allows an attacker to achieve arbitrary code execution by convincing a user to load a specially crafted `.keras` model archive.

Official announcement: Please see the link for details

https://www.tenable.com/cve/CVE-2025-8747

Preface: Nvidia’s security advisories released on August 4, 2025 (e.g., CVE-2025-23318, CVE-2025-23319) are specifically related to the Python backend. The Triton backend for Python. The goal of Python backend is to let you serve models written in Python by Triton Inference Server without having to write any C++ code.

Background: NVIDIA Triton Inference Server is an open-source inference serving software that streamlines the deployment and execution of AI models from various deep learning and machine learning frameworks. It achieves this flexibility through a modular system of backends. 

Each backend within Triton is responsible for executing models from a specific framework. When an inference request arrives for a particular model, Triton automatically routes the request to the necessary backend for execution. 

Key backend frameworks supported by Triton include:

• TensorRT: NVIDIA’s high-performance deep learning inference optimizer and runtime.
• TensorFlow: A popular open-source machine learning framework.
• PyTorch: Another widely used open-source machine learning library.
• ONNX: An open standard for representing machine learning models.
• OpenVINO: Intel’s toolkit for optimizing and deploying AI inference.
• Python: A versatile backend that can execute models written directly in Python and also serves as a dependency for other backends.
• RAPIDS FIL (Forest Inference Library): For efficient inference of tree models (e.g., XGBoost, LightGBM, Scikit-Learn).

This modular backend architecture allows Triton to provide a unified serving solution for a wide range of AI models, regardless of the framework they were trained in.

Vulnerability details:

CVE-2025-23318: NVIDIA Triton Inference Server for Windows and Linux contains a vulnerability in the Python backend, where an attacker could cause an out-of-bounds write. A successful exploit of this vulnerability might lead to code execution, denial of service, data tampering, and information disclosure.

CVE-2025-23319: NVIDIA Triton Inference Server for Windows and Linux contains a vulnerability in the Python backend, where an attacker could cause an out-of-bounds write by sending a request. A successful exploit of this vulnerability might lead to remote code execution, denial of service, data tampering, or information disclosure.

Official announcement: Please see the link for details –

https://nvidia.custhelp.com/app/answers/detail/a_id/5687

Preface: The NVIDIA Triton Inference Server API supports both HTTP/REST and GRPC protocols. These protocols allow clients to communicate with the Triton server for various tasks such as model inferencing, checking server and model health, and managing model metadata and statistics.

Background: NVIDIA Triton™ Inference Server, part of the NVIDIA AI platform and available with NVIDIA AI Enterprise, is open-source software that standardizes AI model deployment and execution across every workload.

The Asynchronous Server Gateway Interface (ASGI) is a calling convention for web servers to forward requests to asynchronous-capable Python frameworks, and applications. It is built as a successor to the Web Server Gateway Interface (WSGI).

NVIDIA Triton Inference Server integrates a built-in web server to expose its functionality and allow clients to interact with it. This web server is fundamental to how Triton operates and provides access to its inference capabilities on both Windows and Linux environments.

Vulnerability details: CVE-2025-23310 – NVIDIA Triton Inference Server for Windows and Linux contains a vulnerability where an attacker could cause stack buffer overflow by specially crafted inputs. A successful exploit of this vulnerability might lead to remote code execution, denial of service, information disclosure, and data tampering.

Official announcement: Please refer to the link for details –

https://nvidia.custhelp.com/app/answers/detail/a_id/5687