Preface: NeMo Curator, part of the NVIDIA NeMo software suite for managing the AI agent lifecycle, is a Python library specifically designed for fast and scalable data processing and curation for generative AI use cases such as foundation language model pretraining, text-to-image model training, domain-adaptive pretraining (DAPT), supervised fine-tuning (SFT) and parameter-efficient fine-tuning (PEFT).

Background: To install the NeMo Curator library, run the following command:

• git clone https://github[.]com/NVIDIA/NeMo-Curator[.]git
• cd NeMo-Curator
• pip install –extra-index-url https://pypi[.]nvidia[.]com “.[cuda12x]”

Data download: downloading pipeline in NeMo Curator consists of the following classes:

• DocumentDownloader: Abstract class for downloading remote data to disk.
• DocumentIterator: Abstract class for reading dataset raw records from the disk.
• DocumentExtractor: Abstract class for extracting text records, as well as any relevant metadata from the records on the disk.

Vulnerability details: NVIDIA NeMo Curator for all platforms contains a vulnerability where a malicious file created by an attacker could allow code injection. A successful exploit of this vulnerability might lead to code execution, escalation of privileges, information disclosure, and data tampering.

Ref: The vulnerability arises when malicious files—such as JSONL files—are loaded by NeMo Curator. If these files are crafted to exploit weaknesses in how NeMo Curator parses or processes them, they can inject executable code. This aligns with your description of:

• Embedded malicious payloads in JSONL files.
• JSON injection attacks exploiting parsing logic.

Official announcement: Please see the link for details –

https://nvidia.custhelp.com/app/answers/detail/a_id/5690

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