Preface: The Ryzen 7000 desktop and laptop chips were introduced in 2023. Alongside the main x86 CPU, Ryzen 7000 has a new type of coprocessor, a Neural Processing Unit (NPU), based on the XDNA™ AI Engine architecture. This new NPU is called Ryzen AI.

Background:

1.Install NPU Drivers

2.Download the NPU driver installation package NPU Driver

3.Install the NPU drivers by following these steps:

4.Extract the downloaded “NPU_RAI1.2.zip” zip file.

5.Open a terminal in administrator mode and execute the [[.]\npu_sw_installer[.]exe] exe file.

6.Ensure that NPU MCDM driver (Version:32.0.201.204, Date:7/26/2024) is correctly installed by opening Device Manager -> Neural processors -> NPU Compute Accelerator Device.

Vulnerability details:

CVE-2025-0014: Incorrect default permissions on the AMD Ryzen™ AI installation folder could allow an attacker to achieve privilege escalation, potentially resulting in arbitrary code execution.

CVE-2024-36337: nteger overflow within AMD NPU Driver could allow a local attacker to write out of bounds, potentially leading to loss of confidentiality, integrity or availability.

CVE-2024-36328: nteger overflow within AMD NPU Driver could allow a local attacker to write out of bounds, potentially leading to loss of integrity or availability.

CVE-2024-36336: nteger overflow within the AMD NPU Driver could allow a local attacker to write out of bounds, potentially leading to a loss of confidentiality, integrity, or availability.

Official announcement: Please refer to the official announcement for details – https://www.amd.com/en/resources/product-security/bulletin/amd-sb-7037.html

Preface: The torch[.]nn[.]MaxPool2d function in PyTorch is used to apply a 2D max pooling operation over an input signal, which is typically an image or a batch of images.

• Torch[.]mkldnn_max_pool2d is optimized for Intel’s MKL-DNN (Math Kernel Library for Deep Neural Networks). It leverages specific optimizations for Intel CPUs, which can lead to better performance on those processors. It might have limitations in terms of supported features and is more specialized for performance optimization.

• Torch[.]nn[.]MaxPool2d is a more general implementation that works across different hardware platforms without specific optimizations for Intel CPUs.  It provides more flexibility and is easier to use within the PyTorch ecosystem, supporting various features like padding, dilation, and return indices.

Background: A floating point exception crash when using torch[.]mkldnn_max_pool2d can occur due to several reasons, often related to invalid or extreme values for parameters like kernel size, stride, or padding. Here are some common causes:

1. Invalid Kernel Size: If the kernel size is set to an extremely large value or zero, it can lead to division by zero or other invalid operations, causing a floating point exception.
2. Stride and Padding Issues: Similar to kernel size, setting stride or padding to extreme values can result in invalid calculations. For example, a stride of zero can cause the pooling operation to repeatedly access the same elements, leading to a crash.
3. Input Tensor Dimensions: If the dimensions of the input tensor are not compatible with the specified kernel size, stride, or padding, it can lead to invalid memory access or calculations.

Vulnerability details: A vulnerability, which was classified as problematic, has been found in PyTorch 2.6.0+cu124. Affected by this issue is the function torch[.]mkldnn_max_pool2d. The manipulation leads to denial of service. An attack has to be approached locally. The exploit has been disclosed to the public and may be used.

Official announcement: Please refer to the link for details – https://nvd.nist.gov/vuln/detail/CVE-2025-2953

Preface: Message queues are quite important in machine learning for several reasons:

1. Decoupling Components: They allow different parts of a machine learning system to operate independently. For example, data producers (like sensors or user inputs) can send data to a queue, and data consumers (like preprocessing units or models) can process this data at their own pace.
2. Asynchronous Processing: Machine learning tasks, especially training and inference, can be resource-intensive and time-consuming. Message queues enable these tasks to be processed asynchronously, ensuring that the system remains responsive and efficient.
3. Scalability: By using message queues, you can easily scale your machine learning system. Multiple consumers can process messages from the queue simultaneously, allowing the system to handle larger workloads without bottlenecks.

Background: RabbitMQ can be quite helpful in AI applications! RabbitMQ is an open-source message broker that facilitates communication between different parts of a system asynchronously. Here are some ways it can assist AI:

1. Task Orchestration: RabbitMQ can manage and distribute tasks across various AI models and services, ensuring efficient processing and load balancing.
2. Handling Asynchronous Requests: It can queue up inference requests and process them asynchronously, which is particularly useful for resource-intensive AI models.
3. Data Queuing: RabbitMQ can queue data for processing, allowing AI systems to handle large volumes of data in a controlled manner.

If you enable the management Plugin, you will be able to use not only the webUI but also the HTTP API. In fact, the WebUI is a wrapper around the HTTP API.

Vulnerability details: RabbitMQ is a messaging and streaming broker. Versions prior to 4.0.3 are vulnerable to a sophisticated attack that could modify virtual host name on disk and then make it unrecoverable (with other on disk file modifications) can lead to arbitrary JavaScript code execution in the browsers of management UI users.

When a virtual host on a RabbitMQ node fails to start, recent versions will display an error message (a notification) in the management UI. The error message includes virtual host name, which was not escaped prior to open source RabbitMQ 4.0.3 and Tanzu RabbitMQ 4.0.3, 3.13.8.

Official announcement: Please refer to the link for details –

https://nvd.nist.gov/vuln/detail/CVE-2025-30219