Official Updated 10^th Nov 2025 05:39 AM

Preface: The core design objective of NVIDIA AIStore (AIS) is to provide a high-performance, linearly scalable, and flexible storage solution specifically optimized for large-scale AI/ML and data analytics workloads. NVIDIA AIStore (AIS) provides secure access via a standalone Authentication Server (AuthN) that uses OAuth 2.0 compliant JSON Web Tokens (JWT) for token-based authentication.

The AuthN server is part of the broader NVIDIA AIStore project, which is publicly available on GitHub. It provides token-based secure access using the JSON Web Tokens (JWT) framework.

Background: The security of a signed JWT relies on a secret key (for HMAC algorithms like HS256) or a public/private key pair (for RSA or ECDSA). This key is used to create a digital signature that ensures the token’s integrity and authenticity—proving it has not been tampered with. If the application’s source code, configuration files, or version control system contains this secret key in plain text, it violates the principle of confidentiality for credentials. An attacker who discovers this hard-coded secret.

Vulnerability details: NVIDIA AIStore contains a vulnerability in AuthN. A successful exploit of this vulnerability might lead to escalation of privileges, information disclosure, and data tampering.

Impacts: Escalation of privileges, information disclosure, data tampering

Remediation: Updated to v3.31

Official announcement: Please refer to the link for details –

https://nvidia.custhelp.com/app/answers/detail/a_id/5724

Official Updated 11/10/2025 05:39 AM

Preface: Clients can communicate with Triton using either an HTTP/REST protocol, a GRPC protocol, or by an in-process C API or its C++ wrapper. Triton supports HTTP/REST and gRPC, both of which involve complex header parsing.

In the context of the Open Inference Protocol (OIP), also known as KServe V2 Protocol. The protocol defines a standardized interface for model inference, which implies that compliant inference servers must be capable of parsing incoming requests and serializing outgoing responses according to the protocol’s defined message formats.

Background: To define a parser that filters the payload for Triton using the KServe V2 (Open Inference Protocol), you need to handle the following:

Key Considerations

1.Protocol Compliance – The parser must understand the OIP message format:

-Inference Request: Includes inputs, outputs, parameters.

-Inference Response: Includes model_name, outputs, parameters.

-Data can be in JSON (for REST) or Protobuf (for gRPC).

2.Filtering Logic – Decide what you want to filter:

-Specific tensor names?

-Certain data types (e.g., FP32, INT64)?

-Large payloads (e.g., skip tensors above a size threshold)?

-Security checks (e.g., reject malformed headers)?

3.Shared Memory Handling – If shared memory is used, the parser should:

-Validate shared_memory_region references.

-Ensure the payload does not redundantly include tensor data when shared memory is specified.

Vulnerability details: NVIDIA Triton Inference Server for Linux and Windows contains a vulnerability where an attacker could cause a stack overflow by sending extra-large payloads. A successful exploit of this vulnerability might lead to denial of service.

Official announcement: Please see the official link for details –

https://nvidia.custhelp.com/app/answers/detail/a_id/5723

Preface: The advantages of using Hydra and OmegaConf for configuration management are flexibility, reproducibility, and scalability. Hydra’s ability to override configurations at runtime from the command line and compose them from multiple sources makes it highly flexible.

NeMo uses Hydra/OmegaConf for configuration management, which supports interpolation and sometimes dynamic evaluation.

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:

CVE-2025-23361: NVIDIA NeMo Framework for all platforms contains a vulnerability in a script, where malicious input created by an attacker may cause improper control of code generation. A successful exploit of this vulnerability may lead to code execution, escalation of privileges, information disclosure, and data tampering.

CVE-2025-33178: NVIDIA NeMo Framework for all platforms contains a vulnerability in the bert services component where malicious data created by an attacker may cause a code injection. A successful exploit of this vulnerability may lead to Code execution, Escalation of privileges, Information disclosure, and Data tampering.

Ref: CVE-2025-33178 in the BERT services component is conceptually similar to CVE-2025-23361 in the LLM pretraining workflow. Both share the same underlying weakness: unsanitized dynamic code generation or execution based on user-controlled input.

Official announcement: Please see the official link for details – https://nvidia.custhelp.com/app/answers/detail/a_id/5718

Preface: NVIDIA Run:ai is a Kubernetes-native platform for managing and optimizing AI workloads, acquired by NVIDIA in 2024. It provides dynamic orchestration for GPU resources, supporting flexible resource allocation to improve resource utilization and accelerate the AI ​​development lifecycle in hybrid, on-premises, and cloud environments. Prior to its acquisition by NVIDIA in December 2024, Run:ai was an Israeli software startup founded in 2018, focused on AI infrastructure management. Its core product is Kubernetes-based software for managing and optimizing GPU workloads for AI applications, helping enterprises utilize hardware more efficiently. The company’s software assists enterprises in managing GPU resources in on-premises, cloud, and hybrid environments. Run:ai does not “change its role in machine learning,” but rather focuses on the key challenges of AI workload management and GPU orchestration throughout the broader Machine Learning Operations (MLOps) lifecycle.

Background: NVIDIA Run:ai is a Kubernetes-based platform that acts as a GPU orchestrator to maximize the efficiency of AI and machine learning workloads. It addresses challenges like managing expensive GPU resources by enabling dynamic, policy-based scheduling, allowing for the sharing of GPUs across teams and projects, and optimizing workload performance for training, tuning, and inference. Run:ai integrates into existing hybrid or on-premises AI infrastructure to improve GPU utilization and accelerate AI development cycles.

NVIDIA DGX Cloud primarily leverages Kubernetes (K8s) for orchestrating and managing AI workloads.

While DGX systems historically used Docker for containerization, DGX Cloud, particularly for advanced AI workloads and resource management, relies on Kubernetes for its scalability, high-performance computing capabilities, and efficient GPU resource allocation. This is often integrated with other NVIDIA software like NVIDIA NeMo and NVIDIA Run:ai, and deployed on cloud services such as Amazon Elastic Kubernetes Service (Amazon EKS).

Vulnerability details: NVIDIA RunAI for all platforms contains a vulnerability where a user could cause an improper restriction of communications channels on an adjacent network. A successful exploit of this vulnerability might lead to escalation of privileges, data tampering, and information disclosure.

Official announcement: Please refer to the link for details –

https://nvidia.custhelp.com/app/answers/detail/a_id/5719