Preface: NeMo is an open source PyTorch-based toolkit for research in conversational AI that exposes more of the model and PyTorch internals. Riva supports the ability to import supported models trained in NeMo.

NVIDIA Riva is a GPU-accelerated SDK for building Speech AI applications, customized for your use case, and delivering real-time performance.

Background: NVIDIA Riva does not come with any default user accounts. Instead, it relies on secure access through NVIDIA NGC (NVIDIA GPU Cloud). Users need to log in to NGC to access and deploy Riva services. This ensures that only authorized users can set up and manage Riva deployments.

NVIDIA Riva’s default access control mechanisms are designed to ensure secure deployment and operation. By default, Riva employs:

Role-Based Access Control (RBAC): This allows administrators to define roles and assign permissions to users based on their roles.

There is authentication between NVIDIA NGC and Riva. When you pull Riva container images from NGC, you need to authenticate using your NGC API key. This involves:

1. NGC CLI Configuration: You set up the NGC CLI with your API key, which acts as your authentication credential1.
2. OAuth Token: The username for authentication is $oauthtoken, and the password is your NGC_API_KEY

Vulnerability details:

CVE-2025-23242 – NVIDIA Riva contains a vulnerability where a user could cause an improper access control issue. A successful exploit of this vulnerability might lead to escalation of privileges, data tampering, denial of service, or information disclosure.

CVE-2025-23243 – NVIDIA Riva contains a vulnerability where a user could cause an improper access control issue. A successful exploit of this vulnerability might lead to data tampering or denial of service.

Official announcement: Please see the official link for details –

https://nvidia.custhelp.com/app/answers/detail/a_id/5625

Preface: The symbol ~/. by itself is not a relative path traversal; it simply refers to the home directory of the current user. However, when combined with ./.., it can be part of a relative path traversal.

Relative path traversal involves using sequences like ../ to navigate up the directory hierarchy. For example, ~/. refers to the home directory, and ./.. moves up one directory level from the current directory. So, ~/. ./.. would navigate to the parent directory of the home directory, which can be considered a form of relative path traversal

Background: NVIDIA NeMo is an end-to-end platform designed for developing and deploying generative AI models. This includes large language models (LLMs), vision language models (VLMs), video models, and speech AI. NeMo offers tools for data curation, fine-tuning, retrieval-augmented generation (RAG), and inference, making it a comprehensive solution for creating enterprise-ready AI models. Here are some key capabilities of NeMo LLMs:

1. Customization: NeMo allows you to fine-tune pre-trained models to suit specific enterprise needs. This includes adding domain-specific knowledge and skills, and continuously improving the model with reinforcement learning from human feedback (RLHF).
2. Scalability: NeMo supports large-scale training and deployment across various environments, including cloud, data centers, and edge devices. This ensures high performance and flexibility for different use cases.
3. Foundation Models: NeMo offers a range of pre-trained foundation models, such as GPT-8, GPT-43, and GPT-530, which can be used for tasks like text classification, summarization, creative writing, and chatbots.
4. Data Curation: The platform includes tools for processing and curating large datasets, which helps improve the accuracy and relevance of the models.
5. Integration: NeMo can be integrated with other NVIDIA AI tools and services, providing a comprehensive ecosystem for AI development.

Vulnerability details: NVIDIA Nemo Framework contains a vulnerability where a user could cause a relative path traversal issue by arbitrary file write. A successful exploit of this vulnerability may lead to code execution and data tampering.

Official announcement: Please see the official link for details

https://nvidia.custhelp.com/app/answers/detail/a_id/5623

Last official update on February 28, 2025 at 3:28 PM

Preface: Hopper PPCIe is limited to HGX 8-way systems, where the eight GPUs and four NVSwitches are passed through to one VM. Other topologies are not supported.

Background: The GPU vBIOS can communicate through IOCTL (Input/Output Control) calls. IOCTL is a system call for device-specific input/output operations and other operations which cannot be expressed by regular system calls. In the context of GPU drivers, IOCTLs are used to interact with the GPU hardware, including tasks like memory management, command submission, and mode setting.

CUDA Interprocess Communication (IPC) is not supported in PPCIe mode. Developer tools such as NVIDIA Nsight for profiling are not supported in PPCIe mode.

When an IOCTL contains privileged functionality and is exposed unnecessarily, attackers may be able to access this functionality by invoking the IOCTL.

Vulnerability details: NVIDIA Hopper HGX for 8-GPU contains a vulnerability in the GPU vBIOS that may allow a malicious actor with tenant level GPU access to write to an unsupported registry causing a bad state. A successful exploit of this vulnerability may lead to denial of service.

Official announcement: Please refer to the link for details – https://nvidia.custhelp.com/app/answers/detail/a_id/5561