Category Archives: Potential Risk of CVE

Potential Risk of CVE

CVE-2025-10585: Type Confusion in V8 (22nd Sep 2025)

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Preface: Type confusion is a vulnerability where a program accesses a resource using an incompatible type, leading to unexpected behavior or memory corruption. This often occurs when a program misinterprets the type of data being used, potentially leading to the execution of the wrong code or the disclosure of sensitive information. This can happen due to issues with type casting, memory layout mismatches, or speculative execution, and it’s a common foundation for various software attacks.

Background: V8 is Google’s open source high-performance JavaScript and Web Assembly engine, written in C++. It is used in Chrome and in Node.js, among others. V8 provides the core JavaScript execution environment that Node.js is built upon. It allows Node.js to: Execute JavaScript code outside the browser.

V8 is Google’s high-performance JavaScript engine used in Chrome and Node.js. It compiles JavaScript directly into machine code, optimizing execution through techniques like just-in-time (JIT) compilation. V8 uses multiple tiers of compilers (Ignition, Sparkplug, Maglev, Turbofan) and an efficient garbage collector to manage memory. Its design prioritizes speed and efficiency, making it a key component in modern web development.

Vulnerability details: CVE-2025-10585: Type Confusion in V8. Reported by Google Threat Analysis Group on 2025-09-16. Google has patched the issue, but details are restricted to prevent further exploitation until most users have updated.

Official announcement: Please refer to the link for details

https://chromereleases.googleblog.com/2025/09/stable-channel-update-for-desktop_17.html

IoT, Potential Risk of CVE

CVE-2025-3231: About ARM Mali. Learn more about the details (19th Sep 2025)

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NVD Published Date: 09/08/2025
NVD Last Modified: 09/08/2025

Preface: The Mali kernel driver and userspace libraries are found in different locations depending on whether the system is Android or a general Linux distribution, and also based on the specific Mali GPU generation and the SoC vendor’s implementation.

Background: Mali GPU is a hardware accelerator.

  • It does not run an OS itself.
  • It relies on kernel-space and user-space drivers (like the Mali kernel driver and userspace libraries) to interface with the operating system (Linux, Android, etc.).

ioctl (Input/Output Control) is the primary syscall used by userspace GPU drivers to communicate with the kernel-space driver. It allows sending custom commands and structured data to the driver.

Typical ioctl operations in Mali drivers include:

  • MALI_IOCTL_ALLOC_MEM: Allocate GPU-accessible memory
  • MALI_IOCTL_FREE_MEM: Free previously allocated memory
  • MALI_IOCTL_SUBMIT_JOB: Submit a GPU job (e.g., shader execution)
  • MALI_IOCTL_WAIT_JOB: Wait for job completion
  • MALI_IOCTL_MAP_MEM: Map memory to userspace

Vulnerability details: CVE-2025-3212 is a vulnerability in the kernel driver that interfaces with the Mali GPU. Here’s what that means:

  • The vulnerability is in software, not the hardware.
  • It allows a local non-privileged user to exploit the driver to access freed memory, which could contain sensitive data or allow privilege escalation.
  • The Mali GPU hardware itself is not “vulnerable” in the sense of having a flaw — but it becomes a vector for exploitation because of the flawed driver.

Official announcement: Please refer to the link for details – https://developer.arm.com/documentation/110627/1-0/

AI and ML, Potential Risk of CVE

2025-23316 and CVE-2025-23268: About NVIDIA Triton Inference Server (18th Sep 2025)

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Preface: AI deployment is accelerated by hardware advancements (especially GPUs), ML platforms and MLOps for automation, the use of pre-trained models via transfer learning, containerization and orchestration for scalability, cloud infrastructure providing on-demand resources, and industry collaborations and specialized data partners to streamline various stages of the AI lifecycle.

Background: NVIDIA Triton Inference Server is an open-source inference serving platform whose primary goal is to simplify and accelerate the deployment of AI models in production environments. It aims to provide a unified platform capable of serving models from various machine learning frameworks, such as TensorFlow, PyTorch, ONNX Runtime, and custom backends, enabling flexibility and interoperability.

The “model name” parameter in NVIDIA Triton Inference Server is a crucial identifier used to specify which model a client wishes to interact with for inference requests.

Client API Usage: When using Triton client libraries (e.g., tritonclient[.]grpc or tritonclient[.]http), the model_name parameter is typically a required argument in functions used to send inference requests.

Both backends (Python and DALI) are part of Triton’s modular architecture. The Python backend often acts as a wrapper or orchestrator for other backends, including DALI.

Vulnerability details:

CVE-2025-23316 NVIDIA Triton Inference Server for Windows and Linux contains a vulnerability in the Python backend, where an attacker could cause a remote code execution by manipulating the model name parameter in the model control APIs. A successful exploit of this vulnerability might lead to remote code execution, denial of service, information disclosure, and data tampering.

CVE-2025-23268 VIDIA Triton Inference Server contains a vulnerability in the DALI backend where an attacker may cause an improper input validation issue. A successful exploit of this vulnerability may lead to code execution.

Official announcement: Please see the link for details –

https://nvidia.custhelp.com/app/answers/detail/a_id/5691

IoT, Potential Risk of CVE

CVE-2024-45434 was published on September 12, 2025. You may have heard about it in popular cybersecurity magazine back in July of this year. Let’s take a closer look at it today. (16-09-2025)

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Preface: If an SDK contains a use-after-free (UAF) vulnerability, the consequences can range from minor data corruption and program crashes to severe security issues like arbitrary code execution, information leakage, and privilege escalation, as the vulnerability allows attackers to manipulate previously freed memory, leading to system instability or full compromise.

Background: Classic and Low Energy devices use different hardware and software stacks: Bluetooth® Classic devices can´t communicate with Bluetooth® Low Energy and vice versa.  As for some applications, the power consumption of Bluetooth® Classic is still too high.  The Bluetooth® SIG developed a standard lower power consumption to overcome this obstacle.

Vulnerability details: OpenSynergy BlueSDK (aka Blue SDK) through 6.x has a Use-After-Free. The specific flaw exists within the BlueSDK Bluetooth stack. The issue results from the lack of validating the existence of an object before performing operations on the object (aka use after free). An attacker can leverage this to achieve remote code execution in the context of a user account under which the Bluetooth process runs.

This vulnerability was part of a broader exploit chain called PerfektBlue, which affected millions of vehicles from:

  • Volkswagen (ICAS3)
  • Mercedes-Benz (NTG6)
  • Škoda (MIB3)
  • And an unnamed OEM

Attackers could:

  • Send crafted AVRCP packets post-pairing
  • Trigger the use-after-free
  • Overwrite function pointers
  • Achieve remote code execution (RCE) in the infotainment system

Official announcement: Please see the link for details –

https://www.tenable.com/cve/CVE-2024-45434

Cell Phone (iPhone, Android, windows mobile), IoT, Potential Risk of CVE

A quick look at branch predictor isolation in KVM-QEMU

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Preface: AMD SoCs can have KVM virtualization for embedded multicore systems, especially with ARM-based architectures, as KVM supports various architectures including ARM and has been adapted for embedded platforms. While KVM was initially designed for Intel and AMD x86 processors, its flexibility and portability allowed for ports to the ARM architecture, making it suitable for embedded multicore SoCs.

Ref: The AMD Zen5 branch predictor is a major update for the Zen family, featuring a new “two-ahead” design that allows it to predict up to two branches per clock cycle, a significant increase from the previous “one-ahead” limit.

Background: Any modern 64-bit AMD processor with AMD-V (SVM) virtualization support can utilize KVM-QEMU for virtualization, as KVM is integrated into the Linux kernel and requires only these hardware extensions for acceleration. To use KVM, you need to ensure AMD-V is enabled in your system’s firmware (BIOS/UEFI) and that your Linux distribution’s kernel and QEMU packages are installed and configured.

The branch predictor is a hardware component located within the processor that stores prediction data internally to improve performance by guessing the outcome of conditional branches.

It uses Branch History Buffers (BHT) and Branch Target Buffers (BTB) to keep track of past branch outcomes and the addresses of the branch targets, respectively, which are small, fast on-chip structures, not part of the main memory system.

Vulnerability details: Researchers from ETH Zurich discovered that incomplete isolation of the branch predictor state in AMD CPUs (Zen 1–5) allows a malicious guest VM to influence indirect branch prediction in the host, specifically in user-space components like QEMU.

The attack, named VMSCAPE, enables a guest VM to leak memory from the host QEMU process at a rate of 32 bytes/sec, including sensitive data like disk encryption keys.

Official announcement: For details, please refer to the link – https://www.amd.com/en/resources/product-security/bulletin/amd-sb-7046.html

AI and ML, Potential Risk of CVE

Point of view – NVIDIA’s NVDebug tool, about CVE-2025-23342 and CVE-2025-23343.  (12th Sep 2025)

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Preface: Debug logs may contain user IDs and passwords to provide diagnostic information for failed login attempts, authentication failures, or to trace user activity within an application, but this is a significant security risk and should be avoided. Security best practices dictate that sensitive information like passwords should never be logged in cleartext. Instead, logging should only include non-sensitive user identifiers to help with troubleshooting without exposing credentials

Background: NVIDIA’s NVDebug tool is part of the broader Nsight Systems tool suite and relies on the NVIDIA Data Center GPU Manager (DCGM) library, specifically utilizing it for data collection and diagnostics to assist in troubleshooting and monitoring NVIDIA GPUs.

  • NVDebug is a tool for debugging and profiling NVIDIA GPUs, particularly in data center environments.
  • DCGM is a library for managing and monitoring NVIDIA GPUs in clusters and data centers.

NVDebug uses the DCGM library to gather essential diagnostic data, logs, and health information from the GPUs, enabling detailed analysis of the system’s state and performance.

Vulnerability details:

CVE-2025-23342: The NVIDIA NVDebug tool contains a vulnerability that may allow an actor to gain access to a privileged account . A successful exploit of this vulnerability may lead to code execution, denial of service, escalation of privileges, information disclosure and data tampering.

CVE-2025-23343: The NVIDIA NVDebug tool contains a vulnerability that may allow an actor to write files to restricted components. A successful exploit of this vulnerability may lead to information disclosure, denial of service, and data tampering.

Official announcement: Please see the link for details – https://nvidia.custhelp.com/app/answers/detail/a_id/5696

AI and ML, Potential Risk of CVE

The incorrect authorization described in CVE-2025-23256 may be triggered or facilitated by the underlying flaw CVE-2025-38456. (11th Sep 2025)

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Preface:

  • IPMI is a standardized interface for hardware management, operating via the Baseboard Management Controller (BMC).
  • It supports both in-band (local) and out-of-band (remote) access.
  • BlueField’s reliance on OpenIPMI and IPMItool makes it susceptible to kernel-level vulnerabilities.

Background: The Intelligent Platform Management Interface (IPMI) is a standard interface for hardware management used by system administrators to control the devices and monitor the sensors. For these, it is necessary the IPMI Controller called Baseboard Management Controller (BMC) and a manager software (for example, IPMItool). It provides an interface to manage IPMI functions in a local (in-band) or remote (out-of-band) system.

Vulnerability details:

This advisory explores a potential causal relationship between two recent vulnerabilities:

  • CVE-2025-23256 – A high-severity vulnerability in the NVIDIA BlueField DPU management interface, allowing local attackers to bypass authorization and modify configurations.

https://nvidia.custhelp.com/app/answers/detail/a_id/5655

  • CVE-2025-38456 – A moderate-severity vulnerability in the Linux IPMI subsystem, involving memory corruption due to mishandled pointers in ipmi_create_user().

https://nvd.nist.gov/vuln/detail/CVE-2025-38456

Recommendations

  1. Patch Kernel IPMI Subsystem: Ensure CVE-2025-38456 is mitigated in all systems running BlueField.
  2. Update BlueField Firmware: Apply NVIDIA’s latest firmware updates addressing CVE-2025-23256.
  3. Audit IPMI Access Controls: Review and restrict local access to /dev/ipmi0 and IPMItool.

Potential Risk of CVE, Under our observation

CVE-2025-9999: About TCP-based client/server Networking feature of PcVue. (9th Sep 2025)

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Preface: PcVue is a well-known and highly regarded SCADA solution, renowned for its innovation and user-friendliness, despite facing competition from larger, more dominant SCADA vendors. While not the most popular solution, PcVue’s strengths in user configuration, advanced HMI functionality, and integration capabilities have solidified its position in the market.

Background: Key Objectives of PcVue SCADA

Supervisory Control and Monitoring: To offer a centralized platform for operators to monitor and control complex industrial processes and large-scale infrastructure in real-time.

Data Acquisition and Analysis: To collect, process, and convert raw data into actionable information, providing operators with relevant alerts, reports, and historical data for informed decision-making.

Vulnerability details: Some payload elements of the messages sent between two stations in a networking architecture are not properly checked on the receiving station allowing an attacker to execute unauthorized commands in the application.

Official announcement: Please see the link for details

https://www.pcvue.com/security/#SB2025-4

How to Prevent This:

Input Validation: Never trust client input. Use strict schemas (e.g., JSON Schema).

  • Command Execution Hardening:
  • Avoid os.system() or shell execution.
  • Use safe APIs like subprocess.run() with argument lists.

Authentication & Authorization: Ensure only authorized users can send control commands.

Web Application Firewall (WAF): Detect and block suspicious payloads.

Cell Phone (iPhone, Android, windows mobile), Potential Risk of CVE

CVE-2025-21484: About Qualcomm – Enhanced Validation of Array Index in Multi-Mode Call Processor (8th Sep 2025)

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Preface: PLMNs are public networks, while private networks (NPNs) serve specific users (such as enterprises). SNPNs (Standalone NPNs) are completely independent, dedicated networks that do not rely on the functionality of public PLMNs.

Background: “Qualcomm Multi-Mode Call Processor” is a component of their Modem-RF system, which is a comprehensive 5G module-RF system designed to provide multi-band, multi-mode connectivity for various devices. These integrated solutions combine the cellular modem, RF transceiver, and RF front-end components to enable 5G, 4G LTE, and legacy cellular network support in a single, cohesive platform.

Each PLMN is identified by a PLMN ID, which includes a country code and mobile network code. The UE uses this ID to distinguish between different PLMNs.

Vulnerability details:

Title – Improper Validation of Array Index in Multi-Mode Call Processor

Description – Memory corruption while selecting the PLMN from SOR failed list.

Vulnerability Type – CWE-129 Improper Validation of Array Index

Official announcement: Please refer to the link for details –

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

Best Practices
  • Always validate array indices before access.
  • Use safer memory functions or wrappers that include bounds checking.
  • Monitor heap usage and implement memory pressure handling routines.

AI and ML, Potential Risk of CVE

CVE-2025-23257 and CVE-2025-23258: About NVIDIA DOCA  (4th Sep 2025)

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Preface: An NVIDIA endless “collect-export” loop refers to the standard, continuous operation of the DOCA Telemetry Service (DTS), where telemetry data is perpetually collected and then exported. While high-frequency telemetry (HFT) offers an external, triggered alternative, the standard DTS flow is designed to run indefinitely, collecting data from the Sysfs provider and potentially exporting it via Prometheus or Fluent Bit.

Background: CUDA (Compute Unified Device Architecture) and DOCA (Data Center Infrastructure-on-a-Chip Architecture) are both NVIDIA SDKs, but they serve distinct purposes and target different hardware.

CUDA SDK: Primarily designed for general-purpose computing on NVIDIA GPUs. It enables developers to program accelerated computing applications by leveraging the parallel processing power of GPUs.

DOCA SDK: Built specifically for NVIDIA BlueField Data Processing Units (DPUs) and SuperNICs, aiming to accelerate data center infrastructure tasks. It enables offloading infrastructure-related workloads from the host CPU to the DPU.

DOCA Telemetry Service (DTS) is a DOCA Service for collecting and exporting telemetry data. It can run on hosts and BlueField, collecting data from built-in providers and external telemetry applications. The service supports various providers, including sysfs, ethtool, ifconfig, PPCC, DCGM, NVIDIA SMI, and more.

Ref: The binary data can be read using the /opt/mellanox/collectx/bin/clx_read app, packaged in collectx-clxapidev , a DOCA dependency package.

Vulnerability details:

CVE-2025-23257: NVIDIA DOCA contains a vulnerability in the collectx-clxapidev Debian package that could allow an actor with low privileges to escalate privileges. A successful exploit of this vulnerability might lead to escalation of privileges.

CVE-2025-23258: NVIDIA DOCA contains a vulnerability in the collectx-dpeserver Debian package for arm64 that could allow an attacker with low privileges to escalate privileges. A successful exploit of this vulnerability might lead to escalation of privileges.

Official announcement: Please see the link for details –

https://nvidia.custhelp.com/app/answers/detail/a_id/5655