Preface: While NVIDIA has not “dropped” support for the core OpenClaw framework, in some specific cases they have moved away from its standard form.

Background: Because NemoClaw “bakes” certain variables into the sandbox configuration during onboarding, if they are not correctly scoped or sanitized, they remain accessible to the agent process even though it should be isolated.

As a result, this allows an attacker to exfiltrate critical secrets (like the NVIDIA_API_KEY or TELEGRAM_BOT_TOKEN mentioned) through the agent’s existing communication channels.

To address the vulnerability identified in CVE-2026-24222 (and the related SSRF risk in CVE-2026-24231), admin should use the following CLI flags during sandbox creation or update. These flags, introduced in NemoClaw v0.0.18, are designed to strictly control which host environment variables are “baked” into the sandbox environment.

For details, see attached diagram.

Vulnerability details:

CVE-2026-2422 NVIDIA NemoClaw contains a vulnerability in the sandbox environment initialization component where a remote attacker may cause improper access control by sending prompt-injected content that causes the agent to read and exfiltrate host environment variables not properly restricted during sandbox creation. A successful exploit of this vulnerability may lead to information disclosure.

CVE-2026-24231 NVIDIA NemoClaw contains a vulnerability in the validateEndpointUrl() SSRF protection component where an attacker may cause a server-side request forgery by supplying a crafted endpoint URL referencing the 0[.]0[.]0[.]0/8 address range via a blueprint configuration file or CLI flag. A successful exploit of this vulnerability may lead to information disclosure.

Official announcement: Please refer to the link for details – https://nvidia.custhelp.com/app/answers/detail/a_id/5837

Recommended Action:
NVIDIA has released a software update for NVIDIA NemoClaw to address this issue. Users should update to version v0.0.18 or later immediately, as the privilege escalation fixes are critical.

Preface: NVIDIA FLARE allows research and data scientists to adapt existing ML/DL workflow to federated learning paradigm.

Background: A critical Insecure Direct Object Reference (IDOR) vulnerability was identified in the NVIDIA NVFlare Dashboard (CVE-2026-24178). In federated learning environments—where privacy is paramount (e.g., HIPAA-compliant medical research)—this flaw allowed unauthorized users to bypass access controls and interact with data belonging to other participants.

The Dashboard’s RESTful API previously relied on user-supplied identifiers (such as job_id or user_id) to retrieve records. While the system verified that a user was logged in (Authentication), it failed to verify if that user actually owned or was authorized to access the specific record requested (Authorization). This allowed an attacker to simply change a numeric ID in an API request to view, modify, or delete sensitive information outside their scope.

Vulnerability details: NVIDIA NVFlare Dashboard contains a vulnerability in the user management and authentication system where an unauthenticated attacker may cause authorization bypass through user-controlled key. A successful exploit of this vulnerability may lead to privilege escalation, data tampering, information disclosure, code execution, and denial of service.

Remediation: The Patch
The vulnerability is fully addressed in NVIDIA FLARE SDK v2.7.2. The fix implements Attribute-Based Access Control (ABAC) by:

• Decoupling Trust: The backend no longer trusts the ID provided in the request URL/body as the sole source of authority.
• Enforcing Ownership: Every database query now automatically injects an owner_id or org_id filter derived from a secure, server-side session.
• Silent Rejection: Unauthorized requests now correctly return a 403 Forbidden error, ensuring data isolation between collaborating parties.

Official announcement: Please refer to the link for details –

https://nvidia.custhelp.com/app/answers/detail/a_id/5819

Preface: Self-service AI is a technology that uses AI techniques (such as chatbots, natural language processing (NLP), and machine learning) to enable customers to solve problems or find information themselves anytime, anywhere, without interacting with human customer service. It acts as a digital agent, providing instant assistance through channels such as websites, instant messaging applications, and voice systems.

Background: In the context of Amazon Content Designer, the result of using static-eval depends entirely on the AST (Abstract Syntax Tree) generated from your string and the Context (variables) you provide. The primary goal of static-eval is to return a plain JavaScript value without using the dangerous eval() function.

An Abstract Syntax Tree (AST) in the context of static evaluation (static-eval) is a hierarchical, tree-structured representation of source code that captures its logical and structural meaning without requiring the code to be executed.

In static analysis, the code is parsed into this tree format, allowing tools to traverse, analyze, and manipulate the structure to find potential bugs, security vulnerabilities, or styling issues before runtime.

Vulnerability details: Improper use of the static-eval npm package in the open source solution qnabot-on-aws versions 7.2.4 and earlier may allow an authenticated administrator to execute arbitrary code within the fulfillment Lambda execution context by injecting a crafted conditional chaining expression via the Content Designer interface, which bypasses the intended expression sandbox through JavaScript prototype manipulation. This may grant direct access to backend resources (Lambda environment variables, OpenSearch indices, S3 objects, DynamoDB tables) that are not exposed through normal administrative interfaces. We recommend you upgrade to version 7.3.0 or above.

Official announcement: Please refer to link for details –

https://nvd.nist.gov/vuln/detail/CVE-2026-7191

Preface: When a Pod’s spec[.]schedulerName is set to kai-scheduler, the default scheduler will completely ignore the Pod, and only the KAI Scheduler’s ServiceAccount permissions will intervene to handle the scheduling and resource binding of the task.

Background: The vulnerability stems from how the scheduler tracks and authorizes GPU resources across different namespaces when using the Reservation Pod mechanism .

1.Improper Isolation: In a multi-tenant environment, the scheduler is supposed to enforce strict isolation between namespaces .

2.Cross-Namespace References: The flaw allows an attacker with access to one namespace to craft a pod definition (such as by manipulating annotations or references) that points to or impacts a PodGroup or reservation in a different, unauthorized namespace .

3.Data/Resource Tampering: By setting nvidia[.]com/gpu (an integer resource) while KAI is managing the same physical card via fractional annotations (like gpu-fraction: “0.5”), you could cause the scheduler to miscalculate or “lose track” of the actual hardware state .

Exploitation: An attacker could use these cross-namespace references to “poach” GPUs reserved for other tenants, leading to data tampering (modifying scheduling state) or unauthorized resource access .

Vulnerability details:  NVIDIA KAI Scheduler contains a vulnerability where an attacker could cause improper authorization through cross-namespace pod references. A successful exploit of this vulnerability might lead to data tampering.

Remedy: To mitigate this risk, you should immediately update to NVIDIA KAI Scheduler v0.13.0 or later .

Official announcement: Please refer to link for details –  https://nvidia.custhelp.com/app/answers/detail/a_id/5818

Preface: NVIDIA CUDA-Q is an open-source, hybrid quantum-classical computing platform designed for simulating and controlling quantum processors (QPUs) using GPU acceleration. It acts as a unified programming model, allowing developers to write code in C++ or Python that seamlessly integrates CPUs, GPUs, and various QPU hardware.

Background: The Relationship: Client API vs. Kernel

1. CUDA-Q Client API (The “Host”)

•       Where it runs: On the CPU (Host).

•       What it does: It manages the overall application logic, classical data processing, and the orchestration of quantum tasks. It “calls” the quantum kernels and handles the results.

•       Language: Typically Python or standard C++.

2. CUDA-Q Kernel (The “Device”)

•       Where it runs: On the QPU (Quantum Processing Unit) or a GPU/CPU Simulator.

•       What it does: This is the code marked with __qpu__ (in C++) or @cudaq.kernel (in Python). It contains the actual quantum circuit instructions (Hadamard gates, CNOTs, measurements).

•       Execution: It is compiled into a specific intermediate representation (like QIR) that the backend target understands.

Vulnerability details: NVIDIA CUDA-Q contains a vulnerability in an endpoint where an unauthenticated attacker may cause an out-of-bounds read by sending a maliciously crafted request. A successful exploit of this vulnerability may lead to denial of service and information disclosure.

Official announcement: Please refer to link for details –

https://nvidia.custhelp.com/app/answers/detail/a_id/5820

Ref: To securely manage API keys and mitigate the risk of development environment theft in CUDA-Q projects, consider the following industry-standard practices. This prevents keys from being leaked in the code and provides a buffer in case the laptop environment is compromised.

1. Use Environment Variables This is the most basic and important rule: Never write API keys directly in the code.

• Local development: Edit your [.]bashrc or [.]zshrc file on your system (such as Linux or macOS):

bash export NVQC_API_KEY=”your_secret_key_here”

Preface: NVIDIA DALI (Data Loading Library) is an open-source, high-performance software library designed to accelerate the data preprocessing stage of deep learning applications. It serves as a portable replacement for the built-in data loaders found in popular frameworks like PyTorch, TensorFlow, MXNet, and PaddlePaddle. 

Background: Deserialization of Data (“Unpacking the Suitcase”)

•Definition: Reconstructing a data structure or object from a serialized stream of bytes or text.

•Data State: A linear string or byte stream is converted back into an in-memory object graph that the application can manipulate and work with.

•Purpose: To take received or stored data and make it usable again within an application’s memory.

•What happens: The object’s original state is restored. In many languages (like Java), this process often bypasses the constructor, initializing fields directly.

Vulnerability details: CVE-2026-24156 NVIDIA DALI contains a vulnerability where an attacker could cause a deserialization of untrusted data. A successful exploit of this vulnerability might lead to arbitrary code execution.

Official announcement: Please refer to link for details: –

https://nvidia.custhelp.com/app/answers/detail/a_id/5811

Ref: The primary remedy for CVE-2026-24156 is upgrading to NVIDIA DALI version 2.0 or later. This update is the only officially recognized permanent fix for the high-severity deserialization vulnerability. 

Preface: Unlike ChatGPT, which is a conversational chatbot, OpenClaw is designed to act. It receives a high-level goal, breaks it down into structured tasks, calls APIs, executes shell commands, and iterates until the objective is complete.

Installing OpenClaw (formerly ClawdBot) to collaborate with OpenAI on a smartphone that already contains WhatsApp is designed to achieve autonomous, proactive, and secure personal AI assistance directly within a messaging interface.

Background: OpenClaw’s primary design objective is to transition AI from a passive, conversational interface into a proactive, action-oriented autonomous agent that can independently execute multi-step workflows across a user’s local operating system and external cloud services.

It is architected as an “AI Gateway” or agent runtime rather than a standalone model, serving as the “hands” for an artificial brain by connecting large language models (LLMs) to real-world tools, files, and messaging platforms.

Older versions of the OpenClaw might have stored permissions in a “sticky” historical field (the legacy role fields). Without this check:

•You might revoke an agent’s access in the new dashboard.

•The system might see “no active tokens” and accidentally “fall back” to old settings.

•The agent would regain access you intended to take away.

Vulnerability details: OpenClaw before 2026.3.28 contains a privilege escalation vulnerability in the /pair approve command path that fails to forward caller scopes into the core approval check. A caller with pairing privileges but without admin privileges can approve pending device requests asking for broader scopes including admin access by exploiting the missing scope validation in extensions/device-pair/index[.]ts and src/infra/device-pairing[.]ts.

Official announcement: Please refer to link for details –

https://nvd.nist.gov/vuln/detail/CVE-2026-33579

Preface: DNA models like DNABERT and Evo2 are Genomic Foundation Models (gLMs), which treat the DNA sequence of 4 letters (A (Adenine), C (Cytosine), T (Thymine), and G (Guanine).) as a “language” to learn the fundamental rules, patterns, and “syntax” governing life.

Similar to how Large Language Models (LLMs) like GPT are pre-trained on vast amounts of text to understand English, these DNA models are pre-trained on billions to trillions of base pairs (nucleotides) from diverse species to understand the “grammar” of genomes, including the 98% that is non-coding.

Background: For a DNA repository, NVIDIA BioNeMo (the life sciences extension of NeMo) handles the heavy lifting of transforming raw genetic sequences into “usable intelligence”. It is used for more than just simple normalization; it provides a specialized pipeline for pre-training, fine-tuning, and analyzing genomic data.

Here is how the workflow typically functions for DNA data:

1. Data Preparation & Preprocessing

Instead of generic text normalization, BioNeMo uses specialized scripts to prepare genomic data (like the GRCh38 human genome) for AI.

•Chunking: Breaking long chromosomal sequences into manageable segments (e.g., 512 nucleotides).

•Tokenization: Converting DNA “letters” (A, C, G, T) into numerical tokens. Advanced models like DNABERT-2 use Byte Pair Encoding (BPE) to process sequences up to 5x more efficiently than older methods.

•Standardization: Organizing raw genomic data into structured formats like FASTA or CSV that the training framework can ingest.

2. Categorization & Functional Prediction – details not described here

3. Downstream Analysis – details not described here

Vulnerability details:

CVE-2026-24164 – NVIDIA BioNeMo contains a vulnerability where a user could cause a deserialization of untrusted data. A successful exploit of this vulnerability might lead to code execution, denial of service, information disclosure, and data tampering.

CVE-2026-24165 – NVIDIA BioNeMo contains a vulnerability where a user could cause a deserialization of untrusted data. A successful exploit of this vulnerability might lead to code execution, denial of service, information disclosure, and data tampering.

Official announcement: Please refer to link for details – https://nvidia.custhelp.com/app/answers/detail/a_id/5808

Preface: NVIDIA JetPack and Jetson Linux (formerly L4T – Linux for Tegra) are the foundational software stacks for NVIDIA Jetson AI modules. Jetson Linux provides the essential BSP (bootloader, Linux kernel, Ubuntu rootfs, drivers), while JetPack SDK bundles this with developer tools, libraries (CUDA, TensorRT), and APIs for AI, computer vision, and robotics.

Background: The initrd and root file system (rootfs) unencrypted creates a significant security gap against local physical attacks. In a standard industrial or autonomous deployment, physical access is often the most direct threat to a machine’s integrity.

The Security Gap: Local Physical Access

When a Jetson device is left with its default, unencrypted configuration, an attacker with physical access can easily bypass system protections:

Because the bootloader cannot read encrypted files directly, it must first mount an unencrypted partition containing the kernel and initrd images. Without signing or encryption, these critical files can be replaced via a malicious USB or NVMe drive.

Ref: nvluks-srv-app is a NVIDIA Jetson Linux user-space application used to retrieve a unique, secure passphrase from the Trusted Execution Environment (TEE) to unlock encrypted partitions at boot time. It enables disk encryption on Jetson devices by facilitating secure communication between the normal operating system and the hardware-backed security services (OP-TEE).

Vulnerability details:

CVE-2026-24154 NVIDIA Jetson Linux has vulnerability in initrd, where an unprivileged attacker with physical access could inject incorrect command line arguments. A successful exploit of this vulnerability might lead to code execution, escalation of privileges, denial of service, data tampering, and information disclosure.

CVE-2026-24148 NVIDIA Jetson for JetPack contains a vulnerability in the system initialization logic, where an unprivileged attacker could cause the initialization of a resource with an insecure default. A successful exploit of this vulnerability might lead to information disclosure of encrypted data, data tampering, and partial denial of service across devices sharing the same machine ID.

CVE-2026-24153 NVIDIA Jetson Linux has a vulnerability in initrd, where the nvluks trusted application is not disabled. 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/5797

Preface: While NVIDIA CUDA provides powerful parallel processing capabilities on both Linux and Windows, developers still need to run Windows on top of Linux using virtio-win in several specific environments:

For example: Windows-Exclusive HPC Applications

Many specialized scientific and engineering applications are only developed for Windows and cannot be easily recompiled for Linux.

Background: To programmatically use RhelDoUnMap() while ensuring user requests are correctly validated, you must specifically address the descriptor count validation to prevent buffer overflows. This function is part of the virtio-win drivers used in Red Hat Enterprise Linux environments.

Key Components of virtio-win include Network (NetKVM), Storage (viostor / virtio-scsi), Memory Balloon (balloon), Serial (virtio-serial), Graphics (virtio-gpu), Input (virtio-input) and Guest Agent (qemu-ga). The RhelDoUnMap() function is part of the virtio-win driver suite, specifically within the VioStor (Virtio Storage) driver.

Vulnerability details : A flaw was found in virtio-win. The `RhelDoUnMap()` function does not properly validate the number of descriptors provided by a user during an unmap request. A local user could exploit this input validation vulnerability by supplying an excessive number of descriptors, leading to a buffer overrun. This can cause a system crash, resulting in a Denial of Service (DoS).

Official announcement: Please refer to the link for details –

https://nvd.nist.gov/vuln/detail/cve-2026-5164