Category Archives: Potential Risk of CVE

IoT, Potential Risk of CVE

In-depth analysis of the Android 0-Click vulnerability – CVE-2025-48593. The issue has been resolved.  (20th Nov 2025)

Leave a comment

Published: 2025-11-17

Preface: An HFP (Hands-Free Profile) device is a Bluetooth device that supports the Hands-Free Profile, which allows for hands-free calling and control of a mobile phone, such as a car’s infotainment system or a wireless headset. It enables features like answering, making, and ending calls, as well as voice dialing and call waiting, using the paired phone’s microphone and speaker.

Background: Unlike many security threats that require users to click on malicious links or download files, this vulnerability operates silently without any user intervention.

Android’s Bluetooth module is responsible for managing the device’s Bluetooth protocol stack and settings. This module is part of the AOSP open-source code library and is used by the Android system and manufacturer firmware. Android’s Bluetooth functionality fundamentally relies on a modified Linux kernel, which is the core of the Android operating system. The device drivers for hardware components like Bluetooth reside within the kernel system. In the affected version, the HF client module lacks necessary state and boundary checks when handling the Bluetooth device discovery database. The suspicious part is that bta_hf_client_scb_init() is called during registration and also after disable, and if the timer callback is still active during this transition, it could access freed or partially reinitialized memory.

Vulnerability details: CVE-2025-48593 – In bta_hf_client_cb_init of bta_hf_client_main[.]cc, there is a possible remote code execution due to a use after free. This could lead to remote code execution with no additional execution privileges needed. User interaction is not needed for exploitation.

Official announcement: Please refer to the link for details – https://www.tenable.com/cve/CVE-2025-48593

Potential Risk of CVE

CVE-2025-52538: About AMD Xilinx Run Time (XRT) 19th Nov 2025

Leave a comment

Last updated: November 11, 2025

Preface: AMD Xilinx refers to the former independent company Xilinx, which was acquired by Advanced Micro Devices (AMD) in February 2022.

Xilinx’s current and past product lines include: Field-Programmable Gate Arrays (FPGAs), System-on-Chip (SoC) Devices, Adaptive Compute Acceleration Platforms (ACAPs), Data Center Accelerator Cards & System-on-Modules (SoMs).

Xilinx provides countless meta layers that enable developers to build all the necessary components for running Linux on Xilinx SoCs.

Background: In XRT, the xocl driver manages device memory through the abstraction of buffer objects (BOs), which are allocated using specific I/O control (ioctl) commands from user space via the XRT core library APIs. User-facing applications do not directly interact with kernel functions, but use the XRT API to manage memory.

Device memory allocation is modeled as buffer objects (bo). For each bo driver tracks the host pointer backed by scatter gather list – which provides backing storage on host – and the corresponding device side allocation of contiguous buffer in one of the memory mapped DDRs/BRAMs, etc.

Remark: The xocl driver is a key Linux kernel component of XRT specifically designed for PCIe-based platforms, managing user-facing functions and communication with the FPGA.

Vulnerability details: CVE-2025-52538 – Improper input validation within the XOCL driver may allow a local attacker to generate an integer overflow condition, potentially resulting in loss of confidentiality or availability.

From a cybersecurity perspective:

•       The XOCL driver manages device memory via buffer objects (BOs) and uses ioctl commands for allocation.

•       The vulnerability occurs because size calculations for BOs were not properly validated, leading to potential overflow when adding offsets or sizes.

•       AMD’s patch reportedly adds stricter input validation and bounds checking before performing arithmetic operations.

Official announcement: Please refer to the link for details –

https://www.amd.com/en/resources/product-security/bulletin/amd-sb-8014.html

Potential Risk of CVE

CVE-2025-42890 – Design flaws of SQL Anywhere Monitor (18th Nov 2025)

Leave a comment

NVD Last Modified: 11/12/2025

Preface: The original graphical interface for the SQL Anywhere Monitor relied on Adobe Flash, which reached its end-of-life in December 2020. Consequently, the GUI interface is no longer provided in modern versions, and users must use non-GUI methods. SAP recommends using SAP SQL Anywhere Cockpit or SAP Solution Manager as modern, supported tools to replace the legacy SQL Anywhere Monitor for comprehensive monitoring requirements.

Background: Sybase SQL Anywhere (now renamed SAP SQL Anywhere) allows hardcoding credentials in various profiles, connection strings, and scripts, but this poses serious security risks and is strongly discouraged. Passwords stored in this way are typically in plaintext or only slightly obfuscated, making them easily accessible to unauthorized users.

ODBC Data Sources (DSNs): The Windows ODBC Data Source Administrator allows saving the username and password in the DSN configuration.

Ref: SAP SQL Anywhere has historically been a popular choice for specific use cases due to its strengths in embedded and mobile applications with intermittent connectivity. However, its overall market popularity has declined, and it is generally considered a legacy system, with mainstream maintenance ending in the near future (around 2028).

Vulnerability details: SQL Anywhere Monitor (Non-GUI) baked credentials into the code,exposing the resources or functionality to unintended users and providing attackers with the possibility of arbitrary code execution.This could cause high impact on confidentiality integrity and availability of the system.

Official announcement: Please refer to the link for details –

https://nvd.nist.gov/vuln/detail/CVE-2025-42890

AI and ML, Potential Risk of CVE

CVE-2025-33185: About NVIDIA AIStore  (17th Nov 2025)

Leave a comment

Official Updated 10th 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

AI and ML, Potential Risk of CVE

CVE-2025-33202: About NVIDIA Triton Inference Server (14th Nov 2025)

Leave a comment

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

AI and ML, Potential Risk of CVE

CVE‑2025‑23361 and CVE-2025-33178: NVIDIA Nemo Framework contains vulnerabilities (13th Nov 2025)

Leave a comment

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

Potential Risk of CVE

CVE-2025-12907: About Devtools in Google Chrome  (12th Nov 2025)

Leave a comment

Preface: Google Chrome comes with DevTools built directly into the browser by default. These are a comprehensive set of web developer tools that allow users to inspect and debug web pages, analyze network activity, monitor performance, and much more.

You can use the snippets option available in the Sources tab in Chrome DevTools. Just type out your code as you do in a code editor and hit Ctrl+Enter (or click on the Run snippet button available there) to execute the code, and the output displays in the console at the bottom.

Background: In Chrome DevTools, “Copy as cURL” is a feature in the Network panel that lets you right-click on a network request and copy it as a curl command. This command replicates the HTTP request outside the browser, including:

  • URL
  • HTTP method (GET, POST, etc.)
  • Headers (cookies, authorization tokens, etc.)
  • Payload (for POST/PUT requests)

Vulnerability details: Insufficient validation of untrusted input in Devtools in Google Chrome prior to 140.0.7339.80 allowed a remote attacker to execute arbitrary code via user action in Devtools. (Chromium security severity: Low)

This CVE refers to a design flaw in Chrome DevTools that could allow unauthorized access or privilege escalation under certain conditions. Based on the advisory, the flaw likely involves:

  • Improper isolation or authentication when DevTools is exposed.
  • Attackers exploiting CDP endpoints without proper permission checks.
Best-Practice Checklist
  1. Always update to Chrome ≥ 140[.]0[.]7339[.]80.
  2. Launch with --remote-debugging-port and --user-data-dir (non-default).
  3. Avoid exposing the CDP port; use localhost only or protected proxies like devtoolium.
  4. Audit DevTools features (e.g., “Copy as cURL”) before exposing them via CDP.
  5. For automation, consider Chrome for Testing for safer, isolated CDP usage.

Official announcement: Please refer to the link for details –

https://www.tenable.com/cve/CVE-2025-12907

AI and ML, Application Development, Potential Risk of CVE

CVE-2025-12863: About the libxml2 XML parsing library (11th Nov 2025)

Leave a comment

Preface: Libxml2, a C library for parsing and manipulating XML documents, can be relevant in machine learning contexts when dealing with data stored or exchanged in XML format. While not a machine learning library itself, libxml2, or its Python binding lxml, serves as a foundational tool for data preparation and feature engineering.

Ref: The “difference” between Libxml and Libxml2 is that they are essentially the same thing, with “libxml2” being the official and specific name for the library.

Background: Moving nodes between XML documents can happen in machine learning workflows, especially during data integration, comparison, or transformation.  

When would you be at risk?

You’d be at risk if:

  • You use libxml2 or lxml to move nodes from one document to another (e.g., merging XML trees).
  • The underlying library internally calls xmlSetTreeDoc() during such operations.
  • The original document gets freed while namespace pointers still reference it.

Vulnerability details: A flaw was found in the xmlSetTreeDoc() function of the libxml2 XML parsing library. This function is responsible for updating document pointers when XML nodes are moved between documents. Due to improper handling of namespace references, a namespace pointer may remain linked to a freed memory region when the original document is destroyed. As a result, subsequent operations that access the namespace can lead to a use-after-free condition, causing an application crash.

Official announcement: Please refer to the link for details.

https://www.tenable.com/cve/CVE-2025-12863

https://access.redhat.com/security/cve/cve-2025-12863

Potential Risk of CVE

CVE-2025-62161: About Youki, a container runtime written in Rust (10th Nov 2025)

Leave a comment

Preface: Amazon, Google, Microsoft, Apple, Cloudflare, Coursera, Discord, Dropbox, and Figma are among the large companies employing Rust for various purposes, including cloud infrastructure, operating system components, web services, and low-level security components.

Youki is gaining increasing attention as a container runtime, especially in the Rust ecosystem, but it has not yet achieved the widespread adoption of mature runtimes such as runc or crun.

runc is a common and fundamental component in Docker environments. runc is the default low-level container runtime used by Docker Engine. Kubernetes, through its Container Runtime Interface (CRI), interacts with a CRI-compliant runtime like containerd or CRI-O. So, while Kubernetes doesn’t directly interact with runc, it relies on higher-level runtimes that use runc for the low-level container operations.

Background: A new container runtime in Rust. youki has the potential to be faster and use less memory than runc. While runc’s memory usage during container initialization can be around 2.2-3MB, Youki aims to reduce this footprint.

Rust may not be the primary language for data scientists, but it plays a vital role in providing the underlying infrastructure for training and deploying large AI models.

Vulnerability details: Youki is a container runtime written in Rust. In versions 0.5.6 and below, the initial validation of the source /dev/null is insufficient, allowing container escape when youki utilizes bind mounting the container’s /dev/null as a file mask.

Remedy: This issue is fixed in version 0.5.7.

Official announcement: Please refer to the link for details –

https://www.tenable.com/cve/CVE-2025-62161

Potential Risk of CVE, Under our observation

About CVE-2025-47365: Qualcomm integrated with automotive platform (7th Nov 2025)

Leave a comment
Published: 11/03/2025

Preface: GM’s Ultra Cruise system is supposed to be a more capable iteration of its Super Cruise ADAS that was first introduced in 2017.

To dig into the weeds a bit, the Ultra Cruise compute, which is about the size of two laptops stacked together, is made up of two Snapdragon SA8540P SoCs and one SA9000P AI accelerator to deliver low-latency control functions on 16-core CPUs and AI compute of more than 300 Tera operations per second for camera, radar and lidar processing.

Background: The Snapdragon Ride Platform accelerates the shift to software-defined vehicles by empowering developers to create AI-driven automated driving solutions. The system-on-chip, called Snapdragon Ride Platform, was developed for advanced driver assistance systems (ADAS) and automated driving. It’s one of a suite of cloud-connected platforms introduced by Qualcomm.

The Qualcomm Cloud AI 100/AIC100 family of products (including SA9000P – part of Snapdragon Ride) are PCIe adapter cards which contain a dedicated SoC ASIC for the purpose of efficiently running Artificial Intelligence (AI) Deep Learning inference workloads. They are AI accelerators.

Qualcomm’s Snapdragon SA8540P SoCs and SA9000P AI accelerator use the QNX Neutrino RTOS for safety-critical functions in automated driving systems.

Vulnerability details: Integer Overflow or Wraparound in Automotive Platform

Description – Memory corruption while processing large input data from a remote source via a communication interface.

Official announcement: Please refer to link for details –

https://docs.qualcomm.com/product/publicresources/securitybulletin/november-2025-bulletin.html