Updated 11/19/2025 09:03 AM

Preface: NVIDIA Isaac is an AI robot development platform consisting of NVIDIA-accelerated libraries, application frameworks, and AI models that accelerate the development of AI robots such as autonomous mobile robots (AMRs), arms and manipulators, and humanoids.

NVIDIA Isaac GR00T N1 is the world’s first open foundation model for generalized humanoid robot reasoning and skills. This cross-embodiment model takes multimodal input, including language and images, to perform manipulation tasks in diverse environments.

Background:

Isaac GR00T N1.5 uses vision and text transformers to encode the robot’s image observations and text instructions. The architecture handles a varying number of views per embodiment by concatenating image token embeddings from all frames into a sequence, followed by language token embeddings.

A token is the most fundamental data unit in a text document, essential for enabling AI to understand and process information. In Natural Language Processing (NLP), tokenization refers to breaking down larger texts into smaller, manageable pieces called tokens.

Please refer to the attached diagram:

The System 1 reasoning module is a pre-trained Vision-Language Model (VLM) that runs on NVIDIA GPU. It processes the robot’s visual perception and language instruction to interpret the environment and understand the task goal. Subsequently, a Diffusion Transformer, trained with action flow-matching, serves as the System 2 action module. It cross-attends to the VLM output tokens and employs embodiment specific encoders and decoders to handle variable state and action dimensions for motion generation.

Vulnerability details: See below –

CVE-2025-33183         NVIDIA Isaac-GR00T for all platforms contains a vulnerability in a Python component, where an attacker could cause a code injection issue. A successful exploit of this vulnerability might lead to code execution, escalation of privileges, information disclosure, and data tampering.

CVE-2025-33184         NVIDIA Isaac-GR00T for all platforms contains a vulnerability in a Python component, where an attacker could cause a code injection issue. A successful exploit of this vulnerability might lead to code execution, escalation of privileges, information disclosure, and data tampering.

Official announcement: Please refer to the link for details –

https://nvidia.custhelp.com/app/answers/detail/a_id/5725

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