Preface: Does Big Data use the TLB in the Linux kernel?

Yes, big data applications in Linux utilize the Translation Lookaside Buffer (TLB) as a crucial component of memory management. The TLB speeds up address translation by caching recently used virtual-to-physical address mappings. Applications like databases, which often handle large datasets and have specific memory access patterns, can benefit from the TLB’s ability to reduce the overhead of accessing physical memory.

Background: The Linux kernel’s mm/hugetlb directory contains the code for Huge TLB (Translation Lookaside Buffer) support. This feature allows the kernel to use larger page sizes (like 2MB or 1GB instead of the usual 4KB) for memory management, potentially improving performance by reducing TLB misses.

Ref: syscalls are part of the operating system kernel and provide an interface for user space programs to request services from the kernel. User space refers to the memory area where applications run, while kernel space is where the operating system’s core and privileged operations reside.

Vulnerability details: In the Linux kernel, the following vulnerability has been resolved: mm/hugetlb: fix huge_pmd_unshare() vs GUP-fast race huge_pmd_unshare() drops a reference on a page table that may have previously been shared across processes, potentially turning it into a normal page table used in another process in which unrelated VMAs can afterwards be installed. If this happens in the middle of a concurrent gup_fast(), gup_fast() could end up walking the page tables of another process. While I don’t see any way in which that immediately leads to kernel memory corruption, it is really weird and unexpected. Fix it with an explicit broadcast IPI through tlb_remove_table_sync_one(), just like we do in khugepaged when removing page tables for a THP collapse.

Official announcement: Please see the link for details https://nvd.nist.gov/vuln/detail/CVE-2025-38085

My comment: If your system is running a stable, older Linux kernel that predates the tlb_remove_table_sync_one() addition. The kernel will not call tlb_remove_table_sync_one() because it doesn’t exist in that version. The new kernel will enforce stricter synchronization, which could affect performance or expose latent bugs. You can make your decision to patch or remain unchanged.

Preface: AI and machine learning workloads rely on optimized object storage to handle the massive, unstructured datasets needed for training and operation. NVIDIA AIStore (AIS) aims to overcome the limitations of traditional filesystems in handling large AI datasets by providing a distributed storage system that can handle the demands of modern AI models.

Background: An AIStore (AIS) target node primarily stores and manages user data, object replicas, and erasure-coded slices. It also handles bucket metadata and other persistent data structures. Essentially, it acts as a storage server within an AIS cluster.

To set up a service account for NVIDIA AIStore running inside Kubernetes, especially for storage services, you’ll typically follow these steps:

(1) The AIS Operator manages the lifecycle of AIStore clusters, including storage provisioning and access control.

(2) Create a Kubernetes Service Account.

(3) Bind Roles to the Service Account

(4) Configure AIStore to Use the Service Account

(5) Ensure Persistent Volumes Are Set Up

Vulnerability details: NVIDIA AIStore contains a vulnerability in the AIS Operator where a user may gain elevated k8s cluster access by using the ServiceAccount attached to the ClusterRole. A successful exploit of this vulnerability may lead to information disclosure.

Official announcement: Please see the link for details – https://nvidia.custhelp.com/app/answers/detail/a_id/5660

Preface: What Does “Linear” Mean in Machine Learning? In the context of machine learning and neural networks:

A linear function is one where the relationship between inputs and outputs can be represented as a straight line (in 2D), or more generally, a hyperplane in higher dimensions.

Background: NVIDIA Megatron-LM is an open-source framework designed for training large transformer models, particularly those with billions of parameters, across distributed GPU architectures. It leverages techniques like tensor and pipeline parallelism to enable efficient training of these massive models.

* Pipeline parallelism is when different stages of a process are executed in separate devices simultaneously. For instance, in the context of Machine Learning, various layers of a model can be distributed across different devices to create a pipeline.

Vulnerability details:

CVE-2025-23264: NVIDIA Megatron-LM for all platforms contains a vulnerability in a python component where an attacker may cause a code injection issue by providing a malicious file. A successful exploit of this vulnerability may lead to Code Execution, Escalation of Privileges, Information Disclosure and Data Tampering.

CVE-2025-23265: NVIDIA Megatron-LM for all platforms contains a vulnerability in a python component where an attacker may cause a code injection issue by providing a malicious file. A successful exploit of this vulnerability may 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/5663