Publication date of this article: June 22, 2026

Preface: Liquid[.]js (LiquidJS) is not a machine learning framework or artificial intelligence library; it is a JavaScript implementation of the Liquid template language. Liquid was originally created by Shopify for loading dynamic content on store pages. However, due to its widespread use in building web interfaces and handling automated processes, it has been combined with machine learning and artificial intelligence in several specific ways.

Background: In a typical web application, LiquidJS runs first to build the skeleton of the page, and JavaScript runs second to make that skeleton come alive.

1. Step 1 (Server): LiquidJS parses an HTML file, injects a user’s name from a database into a template, and outputs a flat HTML string.
2. Step 2 (Network): The server sends this flat HTML across the internet to the user’s device.
3. Step 3 (Browser): The browser displays the HTML and encounters a <script> tag. It then executes the JavaScript code to handle interactive menus or popups on that page.

The developer of LiquidJS included the regex purely to implement a built-in convenience filter called strip_html.

To do this lightweight operation quickly without adding heavy HTML parsing libraries, the developer used the exact regex alternation pattern.

/<script[\s\S]*?<\/script>|<style[\s\S]*?<\/style>|<[\s\S]*?>|<!–[\s\S]*?–>/g

Attackers can exploit the generic |<[\s\S]*?>| group using malformed attributes that regex cannot naturally handle.

Vulnerability details: Because LiquidJS runs directly on the Node.js backend of these cloud apps, the flawed regex becomes a critical threat vector. Node.js is single-threaded. When a backend server processes an unclosed string like <script<script… using that flawed regex, the CPU hits 100% trying to calculate the infinite backtracking possibilities. A single bad request can freeze the entire server thread for over 10 seconds, creating an easy, unauthenticated Denial of Service (DoS) exploit. This is so called Event Loop Blocking (ReDoS).

Official announcement: Please refer to the link for details – https://www.tenable.com/cve/CVE-2026-45617

Preface: Backpropagation is not used by a single specific robot, but rather by deep learning architectures and neural network controllers powering many modern autonomous systems. It is the foundational training algorithm used for everything from autonomous wheeled robots and robotic arms to industrial mobile robots.

Background: Backpropagate is a Python library for fine-tuning large language models on a single GPU.

The backpropagate library bundles Reflex as an optional web UI component. The BACKPROPAGATE_UI_AUTH variable fails because it is an application-specific environment variable for the backpropagate library, not a native Reflex configuration. While the backpropagate CLI exports this variable to subprocesses, its underlying Reflex application code lacks the necessary request guards, middleware, or WebSocket checks to read and enforce it.

FastAPI app managed by Reflex. Because Reflex establishes persistent WebSockets to manage state updates and actions, simply blocking standard HTTP requests is not enough; you must intercept the WebSocket connection handshake.

Ref: Does The BACKPROPAGATE bundle Reflex ?

Yes, the backpropagate library bundles Reflex as an optional web UI component. It uses Reflex to provide a local training control plane where you can upload datasets, start or stop model fine-tuning, orchestrate multi-runs, and push models to Hugging Face.

Vulnerability details: Anyone with simple network access to the Web UI port can connect over WebSockets and bypass security entirely to:

Hijack Training: Remotely trigger arbitrary AI training scripts or alter model weights.

Exfiltrate Data: View or steal private training datasets and path locations.

Tamper with Assets: Export internal GGUF formats or unauthorizedly push models straight to the Hugging Face Hub.

Cause Denial of Service (DoS): Overwhelm and crash host environments by filling up local server disk space with junk data.

Remedy:

Permanent Solution: Update the library to backpropagate version 1.2.0 or later, which formally implements the backend security check.

Temporary Workaround: Inject a standard FastAPI authentication middleware class directly into the application server stack to intercept and enforce HTTP Basic authentication checks on the /_event WebSocket endpoint before Reflex maps it.

Official announcement: Please refer to the link for details – https://www.tenable.com/cve/CVE-2026-48797

Preface: NVIDIA NeMo is a widely adopted, end-to-end framework for building, customizing, and deploying generative AI models (LLMs) and conversational AI agents. It is primarily used to tailor open-source models—such as Llama, Mistral, and Google Gemma—using proprietary enterprise data.

Ollama, Mistral, and Google Gemma represent a powerful ecosystem for running local, open-weight Large Language Models (LLMs). Ollama acts as the engine to run models, while Mistral and Gemma are two of the most popular, high-performing model families designed to be efficient enough to run on personal computers.

Ollama itself does not use Nvidia NeMo; Ollama is an open-source runtime designed to pull, manage, and run LLMs (like Llama, Mistral, and Gemma) locally on consumer hardware. However, Mistral and Google actively collaborate with Nvidia, meaning their models frequently utilize Nvidia’s NeMo framework and are accessible via tools like NVIDIA NIM.

Background: A model_weights[.]ckpt file is a checkpoint file that stores the learned parameters (weights and biases) of a neural network. When serialized using Python’s pickle module, the file contains a bytecode payload representing the pickled Python dictionary of arrays, which poses arbitrary code execution risks during deserialization.

The attached infographic details CVE-2026-24252, a critical security vulnerability that could lead to operating system command injection in NVIDIA NeMo for Linux.

How the Exploit Works

1. Malicious file creation: Attackers package custom code into [.]nemo checkpoints.
2. Abusing Pickle: They use Python’s __reduce__ method.
3. Command payload: This method embeds arbitrary shell commands.
4. Target execution: The victim opens the file locally.
5. Deserialization trigger: NeMo calls pickle[.]load() automatically.
6. System compromise: The OS executes the injected command immediately.

Vulnerability details: CVE-2026-24252 NVIDIA NeMo for Linux contains a vulnerability where an attacker may cause OS command injection. A successful exploit of this vulnerability may lead to code execution, data tampering, escalation of privileges and information disclosure.

Remedy: Modern versions of PyTorch and NeMo mitigate this by passing weights_only=True to the loading mechanism. This instructs the deserializer to strictly accept only raw data arrays (like your original np[.]random[.]randn arrays) and explicitly reject any custom Python classes or executable instructions.

Official announcement: Please refer to the link for details – https://nvidia.custhelp.com/app/answers/detail/a_id/5839

Publication date of this article: 12th June 2026

Preface: Because CVE-2025-54509 breaks that cryptographic isolation, it directly undermines the core trust assumption of AMD SEV-SNP because the hypervisor is the only entity that is supposed to be blocked by SEV-SNP, but is granted unauthorized access by this flaw.

Background: To understand why security advisories and researchers (such as the team behind the “Staleus” attack paper) specifically quote a “malicious hypervisor” even though a bare-metal host OS kernel has the exact same native authority.

But the crucial IOMMU only triggers when a peripheral device acts as the master and initiates a transaction over the PCIe/system bus.

How about MMU? MMU protects the system from unauthorized CPU access to memory and device registers.

The official vulnerability note described that an improper access control for register interface in the input-output memory management unit (IOMMU) could allow a privileged attacker. For example, AMD EPYC 9965 is explicitly built for High-Performance Computing (HPC) clusters. The AMD EPYC 9965 is engineered for high-density, multi-tenant computing environments, and it utilizes a massive PCIe 5.0 bus architecture. Processors, especially in heterogeneous systems (CPUs, GPUs, and accelerators) and embedded environments, use non-coherent memory access primarily to achieve higher performance, reduce power consumption, and minimize hardware complexity.

Ref:Non-coherent memory access occurs when multiple agents (like a CPU and a DMA controller) access the same memory location, but their local caches are not automatically synchronized, leading to potential data inconsistencies. It requires software to explicitly manage cache maintenance (flushing or invalidating) to ensure data integrity.

Vulnerability details:

Primary Target Generation – 5th Gen AMD EPYC (Turin) platforms.

Exploitation Core Flaw – Lack of access control rules governing IOMMU memory-coherency configuration registers.

Attack Execution Mechanics – Host flips a bit forcing the AMD Secure Processor (ASP) to use non-coherent lines, pulling stale table data directly from DRAM.

Security Impact Profile – Complete collapse of Reverse Map Table (RMP) validation, causing cross-domain integrity failures.

Primary Remediation – SB-3039 PI microcode patch + operating system level MMIO mapping restrictions.

Official announcement: Please refer to the link for details – https://nvd.nist.gov/vuln/detail/CVE-2025-54509

Preface: The attached diagram illustrates how an attacker could trigger the CVE-2026-24180 and CVE-2026-24181 vulnerabilities. This diagram serves as a visual aid for threat modeling, dividing the attack vector into two main paths within the NVIDIA Data Load Library (DALI) data processing pipeline.

Background: As shown in the figure, the following detailed information explains how this vulnerability occurred.

1. The Deserialization Vector (The “Pickle Bomb”)

Sections 4 and 5 of the diagram map out how an attacker executes arbitrary code using insecure data parsing:

•The Vulnerability Layer: When DALI processes batches or training checkpoints, it relies on Python’s built-in pickle.loads() function to reconstruct data objects.

•The Exploit Execution: An attacker supplies a maliciously crafted dataset or checkpoint file containing a specialized payload. As shown in the code snippet, when pickle.loads() evaluates the serialized byte stream, it invokes the native Python __reduce__ method. This allows the attacker to step outside the memory sandbox and automatically run system commands with the host program’s privileges.

2. The Memory Boundary Vector (Heap Buffer Overflow)

Sections 2 and 3 explain how memory corruption occurs on the backend during media loading:

•The Vulnerability Layer: DALI leverages CPU/GPU-accelerated multimedia codecs (like libjpeg-turbo and nvJPEG) to pre-parse incoming audio tracks and JPEG image segments.

•The Exploit Execution: The software lacks strict bounds validation for input structures. An attacker passes a specialized file containing mutated headers, altered dimensions, or oversized network packets. Because the system does not verify these bounds, the file metadata triggers an integer or buffer mismatch, forcing data to overrun the allocated limits of the heap memory sector. This results in an out-of-bounds write or read sequence, compromising the stability of downstream frameworks like PyTorch, TensorFlow, or MXNet.

Furthermore, a heap-based buffer overflow in a data loading library is almost always caused by improper data validation. It occurs when the library fails to check input bounds—such as when processing image files, network packets, or file headers—allowing crafted data to exceed the allocated heap buffer’s capacity and overwrite adjacent memory.

Vulnerability details:

CVE-2026-24180 NVIDIA DALI contains a vulnerability in a component where an attacker could cause a heap-based buffer overflow. A successful exploit of this vulnerability might lead to code execution, data tampering, denial of service, and information disclosure.

CVE-2026-24181 NVIDIA DALI contains a vulnerability in a component where an attacker could cause an improper index validation. A successful exploit of this vulnerability might lead to code execution, data tampering, denial of service, and information disclosure.

Official announcement: Please refer to the link for details – https://nvidia.custhelp.com/app/answers/detail/a_id/5814

Preface: Flowise is an open-source, low-code tool that enables users to build customized Large Language Model (LLM) orchestration flows and AI agents using a visual, drag-and-drop interface based on LangChain. It allows for rapid development of AI applications without extensive coding, connecting LLMs (OpenAI, Anthropic, Local via Ollama) with tools, vector stores, and memory.

Background: When you install Flowise in a Docker container, the NodeVM sandbox environment is included within that same container. Flowise uses this NodeVM to securely execute custom JavaScript code (e.g., in Custom JS Function nodes) inside its own runtime environment.

NodeVM (a class from sandboxing libraries like vm2) is a software mechanism used inside JavaScript code to run untrusted code in an isolated scope. NVM (Node Version Manager) is a command-line developer utility used to install and switch between different versions of Node.js on a local machine or server.

Best Practices for Production

Enable Strict Sandbox: Always ensure the JAVASCRIPT_SANDBOX environment variable is set to true in your Docker compose file to prevent unrestricted Node module imports.

Limit Container Privileges: Run the Docker container as a non-root user to minimize damage if a sandbox escape occurs.

Restrict Network Egress: Use Docker network policies to block the Flowise container from accessing sensitive internal networks or databases it does not need.

Vulnerability details: Prior to version 3.1.2, POST /api/v1/node-custom-function lacks route-level authorization, allowing any authenticated user or API key to submit arbitrary JavaScript to the Custom JS Function node. When E2B_APIKEY is not configured — the common deployment case — Flowise executes this code inside a NodeVM sandbox. This sandbox can be escaped, allowing an attacker to reach the host process object and execute system commands via child_process. The result is authenticated remote code execution on the Flowise server host.

Remedy: This issue has been patched in version 3.1.2.

Official announcement: Please refer to link for details – https://www.tenable.com/cve/CVE-2026-46442