Published: 2025-08-20

Updated: 2025-08-19

Preface: Apache Knox uses Eclipse Jetty as its embedded web server. When you deploy and execute Apache Knox, it uses Jetty to handle incoming HTTP requests and provide its various features, such as authentication, authorization, and routing to backend Hadoop services.

Ref: Is Hadoop used in AI? Hadoop ecosystems help with the processing of data and model training operations for machine learning applications.

Background: How Jetty Consumes Resources? Apache Knox leverages Eclipse Jetty as its embedded web server. Apache Knox is a reverse proxy and API Gateway that provides a single point of secure access for Apache Hadoop services. It is written in Java and relies heavily on Java for its runtime environment and functionality.

Is the Exploit Related to HTTP Response Buffer Size?

Not directly. The vulnerability does not exploit the size of the HTTP response buffer itself. Instead, it targets the processing logic of incoming HTTP/2 frames. However:

• If Jetty is configured with large buffers or many concurrent streams, the impact of the exploit can be amplified.
• The server may allocate response buffers unnecessarily if it begins processing a request before realizing it’s invalid.

Vulnerability details: In Eclipse Jetty, versions <=9.4.57, <=10.0.25, <=11.0.25, <=12.0.21, <=12.1.0.alpha2, an HTTP/2 client may trigger the server to send RST_STREAM frames, for example by sending frames that are malformed or that should not be sent in a particular stream state, therefore forcing the server to consume resources such as CPU and memory.

Official announcement: Please see the link for details –

https://www.tenable.com/cve/CVE-2025-5115

https://github.com/jetty/jetty.project/pull/13449

Official Updated 08/11/2025 06:16 AM

Preface: GPT-4 offers several key benefits, including improved accuracy, longer context handling, and the ability to process both text and image inputs. It also exhibits stronger guardrails, leading to more reliable and ethical outputs. Additionally, GPT-4 excels in various tasks like professional and academic benchmarks, creative writing, and adapting to user needs.

Background: The Megatron-LM codebase is a framework for training large, powerful transformer language models at scale, developed by NVIDIA. It focuses on efficient, model-parallel (tensor and pipeline) and multi-node pre-training of transformer-based models like GPT, BERT, and T5 using mixed precision

Megatron-LM codebase efficiently trains models from 2B to 462B parameters across thousands of GPUs, achieving up to 47% Model FLOP Utilization (MFU) on H100 clusters.

GPT-4, the latest iteration in OpenAI’s Generative Pre-trained Transformer series, significantly scales up the parameter count compared to its predecessors. While GPT-2 had 1.5 billion parameters and GPT-3 boasted 175 billion, GPT-4 is estimated to have a staggering 1.76 trillion parameters.

The Megatron-LM codebase has successfully benchmarked the training of a 462B parameter model using 6144 H100 GPUs, achieving up to 47% Model FLOP Utilization (MFU).

While this demonstrates the capability of the Megatron-LM framework to train very large models on H100 clusters, the exact number of H100 GPUs used to train GPT-4 is not publicly disclosed. GPT-4 was developed by OpenAI, and they have not released the specific hardware configurations used for its training.

Vulnerability details:

CVE-2025-23305       NVIDIA Megatron-LM for all platforms contains a vulnerability in the tools component, where an attacker may exploit a code injection issue. A successful exploit of this vulnerability may lead to code execution, escalation of privileges, information disclosure, and data tampering.

CVE-2025-23306       NVIDIA Megatron-LM for all platforms contains a vulnerability in the megatron/training/

arguments.py component where an attacker could cause a code injection issue by providing a malicious input. A successful exploit of this vulnerability may lead to code execution, escalation of privileges, information disclosure, and data tampering.

Official announcement: For more information, please refer to the link

https://nvidia.custhelp.com/app/answers/detail/a_id/5685

Official Updated 08/11/2025 06:15 AM

Preface: While the Bible doesn’t specifically mention artificial intelligence, it reminds us that human knowledge and capabilities will increase dramatically in the last days (Daniel 12:4). Building and training neural networks is a cornerstone of modern artificial intelligence, enabling breakthroughs in fields such as computer vision, natural language processing, and robotics.

Background: NVIDIA Merlin Transformers4Rec is a Python library designed for building sequential and session-based recommender systems, leveraging the power of Transformer architectures, particularly for use with PyTorch. It is part of the broader NVIDIA Merlin ecosystem, which provides end-to-end GPU-accelerated solutions for recommender systems.

Transformers4Rec is pre-installed in the merlin-pytorch container that is available from the NVIDIA GPU Cloud (NGC) catalog.

NVIDIA Merlin PyTorch container, available on NVIDIA NGC (NVIDIA GPU Cloud), includes the necessary components for GPU acceleration, including the CUDA Toolkit.

The Merlin PyTorch container allows users to do preprocessing and feature engineering with NVTabular, and then train a deep-learning based recommender system model with PyTorch, and serve the trained model on Triton Inference Server.

Ref: NVTabular and RAPIDS (cuDF/cuML) for preprocessing and feature engineering.

Vulnerability details: NVIDIA Merlin Transformers4Rec for all platforms contains a vulnerability in a python dependency, where an attacker could cause a code injection issue. A successful exploit of this vulnerability might lead to code execution, escalation of privileges, information disclosure, and data tampering.

Official announcement: Please see the link for details

https://nvidia.custhelp.com/app/answers/detail/a_id/5683

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