Category Archives: Potential Risk of CVE

CVE-2025-58185: improper handling of ASN.1 DER encoding (3rd Nov 2025)

Preface: Is ASN-1 still in use? ASN-1 is used to define a large number of protocols. Its most widespread applications remain in telecommunications, cryptography, and biometrics.

ASN.1 is used in protocols like TLS and LDAP/Active Directory because it provides a language and platform independent way to define data structures, making it a standard for interoperability. Its encoding rules, such as Basic Encoding Rules (BER), offer a compact and efficient binary format for data transmission. Additionally, ASN.1 is used to formally define security standards, such as those in X.509 certificates, which are critical for establishing secure connections in TLS and authenticating users in LDAPIt often features in security vulnerabilities involving TLS and LDAP/Active Directory.

Background: ASN.1 (Abstract Syntax Notation dotone) is a standard for defining abstract data types and is used to describe data representation, transmission, and encoding.

ASN.1 includes data type definitions, data description syntax, encoding rules, etc. BER and DER are one type of encoding rule.

DER is a subset of BER, and it defines an encoding method that uses an octet string to represent any ASN.1 value. DER is used for applications that require encoding with a unique octet string, such as calculating digital signatures based on an ASN.1 encoding. DER is defined in Section 8.7 of X.509.

Vulnerability details: When parsing DER payloads, memories were being allocated prior to fully validating the payloads.
This permits an attacker to craft a big empty DER payload to cause memory exhaustion in functions such as asn1.Unmarshal, x509.ParseCertificateRequest, and ocsp.ParseResponse.

Official announcement: Please refer to the link for details –

https://www.tenable.com/cve/CVE-2025-58185

About Chrome (V8 Bug 452296415 with CVE-2025-12036): updated to 141.0.7390.122/.123 for Windows and Mac and 141.0.7390.122 for Linux. (31-10-2025)

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.

Details of the flaw:

  • Vulnerability: The flaw (CVE-2025-12036) is a memory-related weakness in the V8 JavaScript engine.
  • Exploitation: Attackers can exploit this by creating a crafted web page with malicious JavaScript to execute arbitrary code in the browser’s renderer process.
  • Impact: Successful exploitation can lead to data exposure, sandbox escape, or privilege escalation.
  • Affected versions: Any Chrome version on Windows, macOS, or Linux prior to 141.0.7390.122/.123 is vulnerable.
  • Patch status: Google has released an urgent update to address this issue.
  • CVSS Score: The vulnerability has a CVSS score of 7.5, indicating high severity

Official announcement: Please refer to the link for details –

https://chromereleases.googleblog.com/2025/10/stable-channel-update-for-desktop_21.html

AMD ID: AMD-SB-7055RDSEED Failure on AMD “Zen 5” Processors(27th-10-2025)

Preface: The main consequence of an RDSEED failure on AMD Zen 5 processors is instability, crashes, and potentially corrupted data, as this issue affects the processor’s ability to generate high-quality random numbers for cryptography and other sensitive tasks. This has led to the development of Linux kernel patches to temporarily disable RDSEED on affected Zen 5 CPUs until AMD provides a permanent hardware or firmware fix.

Background: RDSEED is a CPU instruction that provides high-entropy random numbers directly from a hardware entropy source, such as the Intel Digital Random Number Generator. It is designed to be used to seed other pseudo-random number generators (PRNGs) for cryptographic applications, ensuring a secure and unpredictable starting point.

RDSEED is a CPU instruction that provides high-entropy random numbers directly from a hardware entropy source, such as the Intel Digital Random Number Generator. It is designed to be used to seed other pseudo-random number generators (PRNGs) for cryptographic applications, ensuring a secure and unpredictable starting point.

Vulnerability Details: AMD was notified of a bug in “Zen 5” processors that may cause the RDSEED instruction to return 0 at a rate inconsistent with randomness while incorrectly signaling success (CF=1), indicating a potential misclassification of failure as success. This issue was initially reported publicly via the Linux kernel mailing list and was not submitted through AMD’s Coordinated Vulnerability Disclosure (CVD) process.

AMD has determined that the 16-bit and 32-bit forms of the RDSEED instruction on “Zen 5” processors are affected. The 64-bit form of RDSEED is not affected. AMD plans to release mitigations for this vulnerability.

Official announcement: Please refer to the link for details –

https://www.amd.com/en/resources/product-security/bulletin/amd-sb-7055.html

Security Bulletin: NVIDIA ConnectX and BlueField (CVE‑2025-23299) – October 2025 (24th Oct 2025)

Preface: Nvidia BlueField is a line of data processing units (DPUs) designed and produced by Nvidia. Initially developed by Mellanox Technologies. DOCA is a consistent and essential resource across all existing and future generations of BlueField DPU and SuperNIC products.

Background: The NVIDIA cloud-native supercomputing platform leverages the NVIDIA BlueField DPU architecture with high-speed, low-latency. The DPU enables native cloud services that let multiple users securely share resources without loss in application performance. HPC and AI communication frameworks and libraries play a critical role in determining application performance. Due to their latency and bandwidth-sensitive nature, offloading the libraries from the host CPU or GPU to the BlueField DPU creates the highest degree of overlap for parallel progression of communication and computation. DOCA is a consistent and essential resource across all existing and future generations of BlueField DPU and SuperNIC products.

DOCA BlueMan dashboard is the web-based interface for managing and monitoring an NVIDIA BlueField DPU (Data Processing Unit).

Vulnerability details: NVIDIA Bluefield and ConnectX contain a vulnerability in the management interface that may allow a malicious actor with high privilege access to execute arbitrary code.

Reference:

While Python itself is memory-safe, the real risk comes from:

  • YAML parsing libraries (like PyYAML) that allow arbitrary object deserialization.
  • C-based extensions or native bindings used by Python that may not enforce memory safety.
  • Improper validation of YAML configuration passed into privileged services like DTS.

Official announcement: Please see the link for details –

https://nvidia.custhelp.com/app/answers/detail/a_id/5684

CVE-2025-11678: About warmcat libwebsockets (Industrial network security – Are you worried about?) 23rd Oct 2025

Published: 2025-10-20

Preface: Contribute Automation Expert is a software-centric industrial automation platform that is vendor-agnostic and based on the IEC 61499 standard. It is designed to make industrial automation more agile, efficient, and flexible by decoupling hardware and software, which allows for the use of components from different manufacturers and simplifies the process of updating and upgrading systems. 

Background: Libwebsockets is a C library that offers a high-performance, lightweight, and versatile way to handle WebSockets, HTTP, and other protocols. Libwebsockets is a strong contender for projects requiring low-level control and performance.

EcoStruxure Automation Expert Software dPAC is a state-of-the-art multi-platform IEC 61499-based control runtime that includes:

  • Event-based, network-transparent automation capability
  • Native process alarm support
  • Modbus/TCP client and server
  • OPC UA client and server b Ethernet/IP scanner
  • WebSocket server

Vulnerability details: Stack-based Buffer Overflow in lws_adns_parse_label in warmcat libwebsockets allows, when the LWS_WITH_SYS_ASYNC_DNS flag is enabled during compilation, to overflow the label_stack, when the attacker is able to sniff a DNS request in order to craft a response with a matching id containing a label longer than the maximum.

The CVE-2025-11678 vulnerability patch you referenced is in the async-dns-parse[.]c file of libwebsockets, which is a C library for WebSocket and related protocols.

Official details: Official documentation and vulnerability reports mention that EcoStruxure Automation Expert is an affected component for this CVE. Please refer to the link for details –

https://libwebsockets.org/git/libwebsockets/commit?id=2bb9598562b37c942ba5b04bcde3f7fdf66a9d3a

https://www.tenable.com/cve/CVE-2025-11678

AMD response to research about Physical Address Bit Leakage on SEV-SNP Systems (22-10-2025)

Official Revision Date: 20-10-2025

Preface: A 3rd Gen AMD EPYC processor uses an integrated memory controller within its System on a Chip (SoC) to connect to DDR4 DRAM modules, rather than having a separate memory controller component. The memory controller is part of the I/O Die (IOD) which is connected to the Core Complex Dies (CCDs) via the Infinity Fabric. Each processor has up to eight memory channels, each capable of supporting up to two DIMM slots, for a maximum of 4TB of memory per socket.

Background: The secure memory area for the RMP (Reverse Map Table) on a 3rd Gen AMD EPYC processor is a protected region within the DDR4 DRAM modules themselves, managed by the processor’s security features. It is not located inside the SoC but is in a secure area of the system’s main RAM, making it part of the dedicated system memory rather than the processor’s on-chip memory.

In a 3rd Gen AMD EPYC processor, the secure memory area for the Reverse Map Table (RMP) is a protected region within the DDR4 DRAM modules themselves, not a separate “hidden area” on the SoC. The secure memory is managed by the processor’s security features, such as the AMD Secure Processor (ASP), to protect the RMP and prevent attacks that could manipulate it.

Vulnerability details: Researchers reported a cache-based side-channel that could allow an unprivileged user process on AMD Secure Encrypted Virtualization with Secure Nested Paging (SEV-SNP) systems to leak up to six bits of physical address information. The researchers also noted that this leakage does not have an immediate security impact.

AMD has determined that the leakage results from Reverse Map Table (RMP) entries being cached in the L1D and L2 caches. Given that at most six physical address bits are exposed, AMD concurs with the researchers that this leakage does not have an immediate security impact.

Ref: In the absence of an immediate fix from AMD, the only way to reduce the risk of cache-based side-channel attacks in SEV-SNP environments is to follow secure memory handling and system-level best practices.

Official announcement: Please see the link for details – https://www.amd.com/en/resources/product-security/bulletin/amd-sb-3036.html

AMD response to research about ‘GhostFetch’ (21-10-2025)

Official Revision Date: 17-10-2025

Preface: SEV-SNP is a security technology for confidential computing that encrypts virtual machine memory. SEV-SNP protects memory contents and integrity, and its security model does not depend on the cache indexing method.

Background: While SEV-SNP provides strong memory encryption and integrity protections, it does not offer built-in hardware protections specifically for PIPT cachesagainst all forms of side-channel attacks. However, AMD has introduced optional mitigations and best practices to reduce exposure:

  • SEV-SNP includes optional features to mitigate indirect branch predictor poisoning, which is a form of side-channel attack. This helps protect against speculative execution vulnerabilities like Spectre.
  • SEV-ES and SEV-SNP encrypt CPU register states during VM exits, preventing leakage of sensitive data through register inspection.
  • The Reverse Map Table (RMP) ensures that only the owner of a memory page can write to it. This prevents memory aliasing and replay attacks, which could otherwise be exploited via cache-based side channels.
  • SEV-SNP uses Page Validation to ensure that guest pages map to only one physical memory page at a time, reducing the risk of inconsistent memory views that could be exploited.

Vulnerability details: Researchers have shared with AMD a paper titled “GhostFetch: Uncovering and Exploiting the Physical-Address-Indexed Prefetcher to Break AMD SEV-SNP” which describes a prefetcher-based hardware side channel attack.

In their paper the researchers describe a method of using shared prefetcher state to determine whether the virtual address of a load matches the expected address, or whether a load access pattern matches an expected stride.  Either check requires multiple runs but potentially results in loss of confidentiality if the targeted code has either a secret dependent branch or a load access pattern that is secret dependent.

AMD’s response:

AMD believes that the researchers have not identified any AMD prefetchers that have not already been disclosed in the Software Optimization Guide and did not identify any new security implications with AMD prefetchers.

Official announcement: Please see the link for details –

https://www.amd.com/en/resources/product-security/bulletin/amd-sb-7047.html

CVE-2025-33182 and CVE-2025-33177: About NVIDIA Jetson Linux and IGX OS (20-10-2025)

Official Updated 10/13/2025 09:19 AM

Preface: Railway applications have traditionally relied on fixed-function embedded computers to perform tasks such as signaling, monitoring, and train control. To bridge this gap, rail operators and system integrators are turning to AI-driven edge computing to meet the growing demand for real-time processing and automation.

Background: The Nvidia Jetson is not just a CPU; it is a complete embedded computing board with both a CPU and a powerful GPU, memory, and other components on a single module. It is a System on Module (SoM) designed for AI and machine learning applications at the edge.

CPU: The Jetson modules contain an ARM-based CPU for general-purpose processing.

GPU: A key feature is the integrated GPU with CUDA cores, which is specialized for parallel processing and AI tasks.

The NVIDIA Jetson Linux Driver Package includes a UEFI-based bootloader. This bootloader is the standard firmware for newer Jetson platforms like Orin and AGX Xavier, replacing the older CBoot system. The UEFI firmware is included with the Linux kernel, drivers, and a root filesystem for the Jetson platform. 

Component of the driver package: The UEFI bootloader is a standard part of the Jetson Linux Driver Package, alongside the Linux kernel, drivers, and utilities.

Support for modern platforms: Support for the UEFI bootloader is included in recent releases of Jetson Linux, such as R35.6.0 and later, for platforms like Jetson AGX Orin, Orin NX, Orin Nano, and others.

Vulnerability details:

CVE-2025-33182: NVIDIA Jetson Linux contains a vulnerability in UEFI, where improper authentication may allow a privileged user to cause corruption of the Linux Device Tree. A successful exploitation of this vulnerability might lead to data tampering, denial of service.

CVE-2025-33177: NVIDIA Jetson Linux and IGX OS contain a vulnerability in NvMap, where improper tracking of memory allocations could allow a local attacker to cause memory overallocation. A successful exploitation of this vulnerability might lead to denial of service.

Official announcement: Please refer to the url for details –

https://nvidia.custhelp.com/app/answers/detail/a_id/5716

CVE-2025-27053: Incorrect Calculation of Buffer Size in HLOS (17th Oct 2025)

Official Published: 10/06/2025

Preface: Microsoft partners with the Android ecosystem by enabling its PlayReady DRM technology for content protection on Android devices, both in apps and web browsers. This integration allows developers to protect premium content like live TV and video-on-demand (VOD) using Android’s Java DrmManagerClient API, while web browsers use the Encrypted Media Extensions (EME) standard to access PlayReady via a Content Decryption Module (CDM). Leading Android device manufacturers often embed a PlayReady client into their devices, and Microsoft provides a Porting Kit to help them do so.

Background: The core of the Android OS operating system is the Android Open Source Project (AOSP), which is free open source software (FOSS) licensed primarily under the Apache License. However, most devices run a proprietary version of Android developed by Google, which comes pre-installed with additional proprietary, closed-source software, most popular Google Mobile Services (GMS), which includes core applications such as Google Chrome, the digital distribution platform Google Play, and the related Google Play Services development platform.

Qualcomm Android source code is divided into development source code and proprietary source code. Proprietary source code is further divided into proprietary non-HLOS software and proprietary HLOS software. HLOS is the High-level Operating System, and non-HLOS software refers to software below the HLOS layer.

Vulnerability details: Memory corruption during PlayReady APP use case while processing TA commands.

CVE-2025-27053 : If the BUFFER_SIZE is incorrectly calculated (e.g., too small for the actual data exchanged), the TA might read or write beyond the allocated memory, leading to memory corruption. The vulnerability CVE-2025-27053 is a memory corruption issue caused by incorrect buffer size calculation in HLOS when processing TA commands in the PlayReady APP usecase. Here’s why it affects Qualcomm Snapdragon chips.

Official announcement: Please refer to the url for details –

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

CVE-2025-23356: about Isaac Lab component of NVIDIA Isaac Sim (16-10-2025)

Preface: The goal of generating synthetic data for robot models is to create a diverse and realistic dataset for training and validating AI systems in a cost-effective and scalable way, helping to overcome the limitations of real-world data collection. This includes creating data for training models, improving their performance, testing for edge cases, and refining them after initial training without needing extensive, time-consuming, or dangerous physical data collection.  

Background: Isaac Sim facilitates three essential workflows: generating synthetic data for training or post-training robot models used for perception, mobility, and manipulation. It also enables validating robot stacks through software and hardware-in-loop testing and enabling robot learning through Isaac™ Lab.

NVIDIA Isaac Lab is an open-source, unified framework for robot learning that helps developers train robot policies using high-fidelity simulation. Built on NVIDIA Isaac Sim and the Omniverse platform, it leverages the power of GPUs for parallel physics simulation and photorealistic rendering to bridge the gap between simulation and real-world training. The framework simplifies common workflows for robot learning, such as reinforcement learning and imitation learning, by providing modular design patterns and a unified set of tools.

Configuring Stable-Baselines3 (SB3) within Isaac Sim, particularly with Isaac Lab, involves setting up the training environment and specifying hyperparameters for your chosen reinforcement learning algorithm.

Vulnerability details: NVIDIA Isaac Lab contains a vulnerability in SB3 configuration parsing. A successful exploit of this vulnerability might lead to code execution, denial of service, escalation of privileges, information disclosure, or data tampering.

Official announcement: Please see the link for details

https://nvidia.custhelp.com/app/answers/detail/a_id/5708