624 matches found
CVE-2025-54330
CVE-2025-54330 affects Samsung Mobile Processor Exynos 1380 (NPU) with an out-of-bounds read in the __is_done_for_me function, through July 2025. Vulnerable component: NPU inside the Exynos 1380 SoC. Impact as per CVSS: Medium (5.3), networked, requires no user interaction, no privileges. No fixe...
CVE-2025-54333
The CVE-2025-54333 issue is in Samsung Mobile Processor Exynos 1380’s NPU, described as an Invalid Pointer Dereference in the get_vs4l_profiler_node function. Connected sources (e.g., PT-2025-45024, Red Hat/NVD/CVE listings) corroborate the vulnerability but do not provide concrete exploit detail...
PT-2025-45022
Name of the Vulnerable Software and Affected Versions Samsung Mobile Processor Exynos 1380 through July 2025 Description An issue exists in the NPU component of Samsung Mobile Processor Exynos. Specifically, an untrusted pointer dereference of src hdr occurs within the copy ncp header function...
CVE-2025-54333
An issue was discovered in NPU in Samsung Mobile Processor Exynos 1380 through July 2025. There is an Invalid Pointer Dereference of node in the getvs4lprofilernode function...
PT-2025-45023
Name of the Vulnerable Software and Affected Versions Samsung Mobile Processor Exynos versions through July 2025 Description An issue exists in the NPU within Samsung Mobile Processor Exynos. A NULL pointer dereference occurs within the npu vertex profileoff function, specifically affecting...
About the security content of watchOS 26.1
About the security content of watchOS 26.1 This document describes the security content of watchOS 26.1. About Apple security updates For our customers' protection, Apple doesn't disclose, discuss, or confirm security issues until an investigation has occurred and patches or releases are availabl...
About the security content of tvOS 26.1
About the security content of tvOS 26.1 This document describes the security content of tvOS 26.1. About Apple security updates For our customers' protection, Apple doesn't disclose, discuss, or confirm security issues until an investigation has occurred and patches or releases are available...
About the security content of tvOS 26.1
About the security content of tvOS 26.1 This document describes the security content of tvOS 26.1. About Apple security updates For our customers' protection, Apple doesn't disclose, discuss, or confirm security issues until an investigation has occurred and patches or releases are available...
About the security content of iOS 26.1 and iPadOS 26.1
About the security content of iOS 26.1 and iPadOS 26.1 This document describes the security content of iOS 26.1 and iPadOS 26.1. About Apple security updates For our customers' protection, Apple doesn't disclose, discuss, or confirm security issues until an investigation has occurred and patches ...
Machine and Deep Learning for Indoor UWB Jammer Localization
Ultra-wideband UWB localization delivers centimeter-scale accuracy but is vulnerable to jamming attacks, creating security risks for asset tracking and intrusion detection in smart buildings. Although machine learning ML and deep learning DL methods have improved tag localization, localizing...
About the security content of macOS Tahoe 26.1
About the security content of macOS Tahoe 26.1 This document describes the security content of macOS Tahoe 26.1. About Apple security updates For our customers' protection, Apple doesn't disclose, discuss, or confirm security issues until an investigation has occurred and patches or releases are...
About the security content of iOS 26.1 and iPadOS 26.1
About the security content of iOS 26.1 and iPadOS 26.1 This document describes the security content of iOS 26.1 and iPadOS 26.1. About Apple security updates For our customers' protection, Apple doesn't disclose, discuss, or confirm security issues until an investigation has occurred and patches ...
About the security content of visionOS 26.1
About the security content of visionOS 26.1 This document describes the security content of visionOS 26.1. About Apple security updates For our customers' protection, Apple doesn't disclose, discuss, or confirm security issues until an investigation has occurred and patches or releases are...
About the security content of watchOS 26.1
About the security content of watchOS 26.1 This document describes the security content of watchOS 26.1. About Apple security updates For our customers' protection, Apple doesn't disclose, discuss, or confirm security issues until an investigation has occurred and patches or releases are availabl...
About the security content of macOS Tahoe 26.1
About the security content of macOS Tahoe 26.1 This document describes the security content of macOS Tahoe 26.1. About Apple security updates For our customers' protection, Apple doesn't disclose, discuss, or confirm security issues until an investigation has occurred and patches or releases are...
About the security content of visionOS 26.1
About the security content of visionOS 26.1 This document describes the security content of visionOS 26.1. About Apple security updates For our customers' protection, Apple doesn't disclose, discuss, or confirm security issues until an investigation has occurred and patches or releases are...
Android Malware Detection: A Machine Learning Approach
This study examines machine learning techniques like Decision Trees, Support Vector Machines, Logistic Regression, Neural Networks, and ensemble methods to detect Android malware. The study evaluates these models on a dataset of Android applications and analyzes their accuracy, efficiency, and...
Towards Ultra-Low Latency: Binarized Neural Network Architectures for In-Vehicle Network Intrusion Detection
The Control Area Network CAN protocol is essential for in-vehicle communication, facilitating high-speed data exchange among Electronic Control Units ECUs. However, its inherent design lacks robust security features, rendering vehicles susceptible to cyberattacks. While recent research has...
A Survey of Heterogeneous Graph Neural Networks for Cybersecurity Anomaly Detection
Anomaly detection is a critical task in cybersecurity, where identifying insider threats, access violations, and coordinated attacks is essential for ensuring system resilience. Graph-based approaches have become increasingly important for modeling entity interactions, yet most rely on homogeneou...
Attention Augmented GNN RNN-Attention Models for Advanced Cybersecurity Intrusion Detection
In this paper, we propose a novel hybrid deep learning architecture that synergistically combines Graph Neural Networks GNNs, Recurrent Neural Networks RNNs, and multi-head attention mechanisms to significantly enhance cybersecurity intrusion detection capabilities. By leveraging the comprehensiv...