Released on February 18, 2025

Preface: In NVIDIA CUDA, cuobjdump and nvdisasm are two binary utilities used for examining and disassembling CUDA binaries (cubin files).

cuobjdump

• Purpose: It can disassemble CUDA binaries and extract PTX (Parallel Thread Execution) code from host binaries, executables, object files, static libraries, and external fatbinary files.
• Usage: cuobjdump is versatile as it accepts both cubin files and host binaries.
• Features: It provides basic disassembly and extraction capabilities but lacks advanced display options and control flow analysis.

nvdisasm

• Purpose: It is specifically designed to disassemble cubin files.
• Usage: Unlike cuobjdump, nvdisasm only accepts cubin files.
• Features: It offers richer output options, including advanced display options and control flow analysis.

These tools are essential for developers who need to inspect and debug the compiled CUDA code.

Background: Parallel processing is a method in computing of running two or more processors (CPUs) to handle separate parts of an overall task. Breaking up different parts of a task among multiple processors will help reduce the amount of time to run a program. GPUs render images more quickly than a CPU because of its parallel processing architecture, which allows it to perform multiple calculations across streams of data simultaneously. The CPU is the brain of the operation, responsible for giving instructions to the rest of the system, including the GPU(s).

NVIDIA CUDA provides a simple C/C++ based interface. The CUDA compiler leverages parallelism built into the CUDA programming model as it compiles your program into code.
CUDA is a parallel computing platform and programming interface model created by Nvidia for the development of software which is used by parallel processors. It serves as an alternative to running simulations on traditional CPUs.

Vulnerability details:

The following two design flaws are associated with these CVEs:

CVE‑2024‑53870, CVE‑2024‑53871, CVE‑2024‑53872, CVE‑2024‑53873, CVE‑2024‑53874, CVE‑2024‑53875, CVE‑2024‑53876, CVE‑2024‑53877, CVE‑2024‑53878 and CVE‑2024‑53879

NVIDIA CUDA toolkit for Linux and Windows contains a vulnerability in the cuobjdump binary, where a user could cause a crash by passing a malformed ELF file to cuobjdump. A successful exploit of this vulnerability might lead to a partial denial of service.

NVIDIA CUDA toolkit for all platforms contains a vulnerability in the nvdisasm binary, where a user could cause an out-of-bounds read by passing a malformed ELF file to nvdisasm. A successful exploit of this vulnerability might lead to a partial denial of service.

Official announcement: Please refer to the vendor announcement for details – https://nvidia.custhelp.com/app/answers/detail/a_id/5594

The original security advisory was issued on February 11, 2025.

Preface: NVIDIA IGX Orin software is used by a variety of organizations, particularly those in industrial and medical environments. This platform is designed to support AI applications at the edge, providing high performance, advanced functional safety, and security.

Some specific use cases include:

• Industrial Automation: Companies use IGX Orin to enhance manufacturing processes with AI-driven automation and predictive maintenance.
• Healthcare: Medical institutions leverage IGX Orin for AI-powered diagnostics, medical imaging, and patient monitoring.
• Robotics: Robotics companies utilize IGX Orin for developing intelligent robots that can operate safely alongside humans.

The platform’s versatility and robust support make it suitable for any organization looking to deploy AI solutions in demanding environments.

Background: The NVIDIA IGX Orin Developer Kit runs the Holopack 2.0 Developer Preview software. Holopack is a comprehensive solution for end-to-end GPU accelerated AI application development and testing. Holopack supports two GPU modes:

iGPU – Holopack deploys drivers and libraries to support NVIDIA Ampere sets on NVIDIA IGX Orin modules into the GPU.

dGPU – Holopack deploys drivers and libraries to support optional NVIDIA RTX A6000 connected to PCIe slot Discrete GPU.

Its high-performance, low-power computing for deep learning, and computer vision makes Jetson the ideal platform for compute-intensive projects. The Jetson platform includes a variety of Jetson modules with NVIDIA JetPack™ SDK.

Vulnerability details: NVIDIA Jetson AGX Orin™ and NVIDIA IGX Orin software contain a vulnerability where an attacker can cause an improper input validation issue by escalating certain permissions to a limited degree. A successful exploit of this vulnerability might lead to code execution, denial of service, data corruption, information disclosure, or escalation of privilege.

Official announcement: Please refer to the vendor announcement for details – https://nvidia.custhelp.com/app/answers/detail/a_id/5611

Original release date: February 11, 2025

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).

Vulnerability details: NVIDIA Triton Inference Server contains a vulnerability in the model loading API, where a user could cause an integer overflow or wraparound error by loading a model with an extra-large file size that overflows an internal variable. A successful exploit of this vulnerability might lead to denial of service.

Official announcement: Please refer to the vendor announcement for details – https://nvidia.custhelp.com/app/answers/detail/a_id/5612

Preface: In software development, time-of-check to time-of-use (TOCTOU, TOCTTOU or TOC/TOU) is a class of software bugs caused by a race condition involving the checking of the state of a part of a system (such as a security credential) and the use of the results of that check.

Background: The NVIDIA container stack is architected so that it can be targeted to support any container runtime in the ecosystem. The components of the stack include:

The NVIDIA Container Runtime (nvidia-container-runtime)

The NVIDIA Container Runtime Hook (nvidia-container-toolkit / nvidia-container-runtime-hook)

The NVIDIA Container Library and CLI (libnvidia-container1, nvidia-container-cli)

The components of the NVIDIA container stack are packaged as the NVIDIA Container Toolkit.

The NVIDIA Container Toolkit is a key component in enabling Docker containers to leverage the raw power of NVIDIA GPUs. This toolkit allows for the integration of GPU resources into your Docker containers.

Remark: The Podman command can be used with remote services using the –remote flag. Connections can be made using local unix domain sockets, ssh

Vulnerability details: NVIDIA Container Toolkit for Linux contains a Time-of-Check Time-of-Use (TOCTOU) vulnerability when used with default configuration, where a crafted container image could gain access to the host file system. A successful exploit of this vulnerability might lead to code execution, denial of service, escalation of privileges, information disclosure, and data tampering.

Official announcement: Please refer to the vendor announcement for detail – https://nvidia.custhelp.com/app/answers/detail/a_id/5616

Preface: RAPIDS™, part of NVIDIA CUDA-X, is an open-source suite of GPU-accelerated data science and AI libraries with APIs that match the most popular open-source data tools. It accelerates performance by orders of magnitude, at scale, across data pipelines.

Background: RAPIDS is an open-source suite of software libraries and frameworks developed by NVIDIA to accelerate and streamline data science and analytics workflows. One of its key components is cuDF, a GPU-accelerated DataFrame library that mirrors the functionality of Pandas but operates at much higher speeds. This allows for rapid data loading, filtering, and transformation with reduced memory usage.

cuDF: Python bindings for libcudf (Pandas like API for DataFrame manipulation)

cuML: C++/CUDA ML Algorithms: C++/CUDA machine learning algorithms

Vulnerability details: NVIDIA RAPIDS contains a vulnerability in cuDF and cuML, where a user could cause a deserialization of untrusted data issue. A successful exploit of this vulnerability might lead to code execution, data tampering, denial of service, and information.

Official announcement: Please refer to the link for details – https://nvidia.custhelp.com/app/answers/detail/a_id/5597

Preface: Humans have integrated smartphones (IoT) into their daily lives due to habit formation. Suddenly one day, the browsers of more than 20% of the people on the planet stopped working for half a day. Maybe you’ll see long queues outside the hospital!

It similar as an intangible control to you. Go to AI age/century, smartphone is the great partner of AI.

Background: Edge was initially built with Microsoft’s own proprietary browser engine, EdgeHTML, and their Chakra JavaScript engine. In late 2018, it was announced that Edge would be completely rebuilt as a Chromium-based browser with Blink and V8 engines.

Chrome used only WebCore, and included its own JavaScript engine named V8 and a multiprocess system. Chrome for iOS continues to use WebKit because Apple requires that web browsers on that platform must do so.

Remark: Edge was originally based on Chakra but has more recently been rebuilt using Chromium and the V8 engine. V8 is written in C++, and it’s continuously improved.

Vulnerability details:

CVE-2025-0612 Out of bounds memory access in V8 in Google Chrome prior to 132.0.6834.110 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: High)

CVE-2025-0611 Object corruption in V8 in Google Chrome prior to 132.0.6834.110 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: High)

Official announcement: Please refer to the link for details

https://nvd.nist.gov/vuln/detail/CVE-2025-0611

https://nvd.nist.gov/vuln/detail/CVE-2025-0612

CVE‑2024‑0146: A design weakness in the Virtual GPU Manager, where a malicious guest could cause memory corruption. (20-1-2025)

Preface: In Kernel mode, the executing code has complete and unrestricted access to the underlying hardware. It can execute any CPU instruction and reference any memory address. Kernel mode is generally reserved for the lowest-level, most trusted functions of the operating system.

If the destination buffer is not large enough, the function will write null characters to the destination buffer to ensure that the string is null-terminated, but this can lead to a buffer overflow if the null characters overwrite adjacent memory locations.

Background: NVIDIA vGPU software enables multiple virtual machines (VMs) to have simultaneous, direct access to a single physical GPU, using the same NVIDIA graphics drivers that are deployed on non-virtualized operating systems.
NVIDIA Virtual GPU (vGPU) enables multiple virtual machines (VMs) to have simultaneous, direct access to a single physical GPU, using the same NVIDIA graphics drivers that are deployed on non-virtualized operating systems. By doing this, NVIDIA vGPU provides VMs with unparalleled graphics performance, compute performance, and application compatibility, together with the cost-effectiveness and scalability brought about by sharing a GPU among multiple workloads.

Vulnerability details: NVIDIA vGPU software contains a vulnerability in the Virtual GPU Manager, where a malicious guest could cause memory corruption. A successful exploit of this vulnerability might lead to code execution, denial of service, information disclosure, or data tampering.

Impact software products:

Citrix Hypervisor, VMware vSphere, Red Hat Enterprise Linux KVM, Ubuntu

Azure Local

Official announcement: Please refer to the link for details – https://nvidia.custhelp.com/app/answers/detail/a_id/5614/~/security-bulletin%3A-nvidia-gpu-display-driver—january-2025

Preface: RAM and Unified Memory are essentially the same thing. Unified Memory is just RAM Built-in CPU chips. It’s unified with the CPU. So 128GB of RAM is adequate to 128GB of unified Memory.

Background: Nvidia designs graphics processing units (GPUs) for the gaming and professional markets, as well as system on a chip units (SoCs) for the mobile computing and automotive market. This page tracks Nvidia drivers, which provide support for their various GPU lineups and are available for Windows, Linux, Solaris, and FreeBSD.

Information leaks are not rare! In Linux kernel, Information leak vulnerabilities are the most prevalent type.Kernel Memory Sanitizer (KMSAN) discovered more than a hundred uninitialized data use bugs.

Vulnerability details:  NVIDIA Unified Memory driver for Linux contains a vulnerability where an attacker could leak uninitialized memory. A successful exploit of this vulnerability might lead to information disclosure.

Official announcement: Please refer to the link for details –

https://nvidia.custhelp.com/app/answers/detail/a_id/5614

Preface: In software development, time-of-check to time-of-use (TOCTOU, TOCTTOU or TOC/TOU) is a class of software bugs caused by a race condition involving the checking of the state of a part of a system (such as a security credential) and the use of the results of that check.

Background: The NVIDIA container stack is architected so that it can be targeted to support any container runtime in the ecosystem. The components of the stack include:

-The NVIDIA Container Runtime (nvidia-container-runtime)

-The NVIDIA Container Runtime Hook (nvidia-container-toolkit / nvidia-container-runtime-hook)

-The NVIDIA Container Library and CLI (libnvidia-container1, nvidia-container-cli)

The components of the NVIDIA container stack are packaged as the NVIDIA Container Toolkit.

The NVIDIA Container Toolkit is a key component in enabling Docker containers to leverage the raw power of NVIDIA GPUs. This toolkit allows for the integration of GPU resources into your Docker containers.

Remark: The Podman command can be used with remote services using the –remote flag. Connections can be made using local unix domain sockets, ssh

Vulnerability details:

CVE-2024-0135 – NVIDIA Container Toolkit contains an improper isolation vulnerability where a specially crafted container image could lead to modification of a host binary. A successful exploit of this vulnerability may lead to code execution, denial of service, escalation of privileges, information disclosure, and data tampering.

CVE-2024-0136 – NVIDIA Container Toolkit contains an improper isolation vulnerability where a specially crafted container image could lead to untrusted code obtaining read and write access to host devices. This vulnerability is present only when the NVIDIA Container Toolkit is configured in a nondefault way. A successful exploit of this vulnerability may lead to code execution, denial of service, escalation of privileges, information disclosure, and data tampering.

CVE-2024-0137 – NVIDIA Container Toolkit contains an improper isolation vulnerability where a specially crafted container image could lead to untrusted code running in the host’s network namespace. This vulnerability is present only when the NVIDIA Container Toolkit is configured in a nondefault way. A successful exploit of this vulnerability may lead to denial of service and escalation of privileges.

Official announcement: Please refer to the vendor announcement for detail – https://nvidia.custhelp.com/app/answers/detail/a_id/5599