Autoeastern Cyclone Matrix TRF security vulnerability

Autoeastern Cyclone Matrix TRF is an automotive smart door lock system from Autoeastern Ecuador. A security vulnerability exists in the Autoeastern Cyclone Matrix TRF that stems from the use of fixed learning code that could lead to replay attacks...

KIA Aftermarket Generic Smart Keyless Entry System security vulnerabilities

KIA Aftermarket Generic Smart Keyless Entry System is an automotive smart door locking system from KIA, a South Korean company. A security vulnerability exists in the KIA Aftermarket Generic Smart Keyless Entry System that stems from the use of fixed learning code, which could lead to replay...

PT-2025-25420 · Unknown · Kia-Branded Aftermarket Generic Smart Keyless Entry System

Name of the Vulnerable Software and Affected Versions: KIA-branded Aftermarket Generic Smart Keyless Entry System versions 2022 through 2025 Description: The issue is related to the use of fixed learning codes in the Key Fob Transmitter, which allows a replay attack. This affects KIA vehicles in...

CVE-2025-43863 vantage6 lacks brute-force protection on change password functionality

vantage6 is an open source framework built to enable, manage and deploy privacy enhancing technologies like Federated Learning and Multi-Party Computation. If attacker gets access to an authenticated session, they can try to brute-force the user password by using the change password functionality...

CVE-2025-43863 vantage6 lacks brute-force protection on change password functionality

vantage6 is an open source framework built to enable, manage and deploy privacy enhancing technologies like Federated Learning and Multi-Party Computation. If attacker gets access to an authenticated session, they can try to brute-force the user password by using the change password functionality...

Differentially Private Relational Learning with Entity-Level Privacy Guarantees

Learning with relational and network-structured data is increasingly vital in sensitive domains where protecting the privacy of individual entities is paramount. Differential Privacy DP offers a principled approach for quantifying privacy risks, with DP-SGD emerging as a standard mechanism for...

Multi-Modal Multi-Task Federated Foundation Models for Next-Generation Extended Reality Systems: Towards Privacy-Preserving Distributed Intelligence in AR/VR/MR

Extended reality XR systems, which consist of virtual reality VR, augmented reality AR, and mixed reality XR, offer a transformative interface for immersive, multi-modal, and embodied human-computer interaction. In this paper, we envision that multi-modal multi-task M3T federated foundation model...

vantage6 security feature vulnerability

vantage6 is a vantage6 open source priVAcy preserviNg federalTed leArningG infrastructure for Secure Insight eXchange. A security feature issue vulnerability exists in vantage6 versions prior to 4.11.0 that stems from an insecure JWT key auto-generation that could lead to key prediction...

vantage6 security vulnerabilities

vantage6 is a vantage6 open source priVAcy preserviNg federalTed leArningG infrastructure for Secure Insight eXchange. A security vulnerability exists in vantage6 versions prior to 4.11 that stems from the change password feature allowing unlimited attempts, which could lead to a brute force atta...

TimberStrike: Dataset Reconstruction Attack Revealing Privacy Leakage in Federated Tree-Based Systems

Federated Learning has emerged as a privacy-oriented alternative to centralized Machine Learning, enabling collaborative model training without direct data sharing. While extensively studied for neural networks, the security and privacy implications of tree-based models remain underexplored. This...

Differentially Private Federated $K$-Means Clustering with Server-Side Data

Clustering is a cornerstone of data analysis that is particularly suited to identifying coherent subgroups or substructures in unlabeled data, as are generated continuously in large amounts these days. However, in many cases traditional clustering methods are not applicable, because data are...

TRIDENT -- a Three-Tier Privacy-Preserving Propaganda Detection Model in Mobile Networks Using Transformers, Adversarial Learning, and Differential Privacy

The proliferation of propaganda on mobile platforms raises critical concerns around detection accuracy and user privacy. To address this, we propose TRIDENT - a three-tier propaganda detection model implementing transformers, adversarial learning, and differential privacy which integrates syntact...

Devil'S Hand: Data Poisoning Attacks to Locally Private Graph Learning Protocols

Graph neural networks GNNs have achieved significant success in graph representation learning and have been applied to various domains. However, many real-world graphs contain sensitive personal information, such as user profiles in social networks, raising serious privacy concerns when graph...

AI-Based Software Vulnerability Detection: a Systematic Literature Review

Software vulnerabilities in source code pose serious cybersecurity risks, prompting a shift from traditional detection methods e.g., static analysis, rule-based matching to AI-driven approaches. This study presents a systematic review of software vulnerability detection SVD research from 2018 to...

ContextBuddy: AI-Enhanced Contextual Insights for Security Alert Investigation (Applied to Intrusion Detection)

Modern Security Operations Centres SOCs integrate diverse tools, such as SIEM, IDS, and XDR systems, offering rich contextual data, including alert enrichments, flow features, and similar case histories. Yet, analysts must still manually determine which of these contextual cues are most relevant...

GPS Spoofing Attacks on AI-Based Navigation Systems with Obstacle Avoidance in UAV

Recently, approaches using Deep Reinforcement Learning DRL have been proposed to solve UAV navigation systems in complex and unknown environments. However, despite extensive research and attention, systematic studies on various security aspects have not yet been conducted. Therefore, in this pape...

Boosting Gradient Leakage Attacks: Data Reconstruction in Realistic FL Settings

Federated learning FL enables collaborative model training among multiple clients without the need to expose raw data. Its ability to safeguard privacy, at the heart of FL, has recently been a hot-button debate topic. To elaborate, several studies have introduced a type of attacks known as gradie...

Striking Back at Cobalt: Using Network Traffic Metadata to Detect Cobalt Strike Masquerading Command and Control Channels

Off-the-shelf software for Command and Control is often used by attackers and legitimate pentesters looking for discretion. Among other functionalities, these tools facilitate the customization of their network traffic so it can mimic popular websites, thereby increasing their secrecy. Cobalt...

How LMS Software Supports Secure Online Employee Learning

Explore how learning management systems LMS software supports safe online learning, protects employee data, and ensures compliance in…...

SoK: Data Reconstruction Attacks against Machine Learning Models: Definition, Metrics, and Benchmark

Data reconstruction attacks, which aim to recover the training dataset of a target model with limited access, have gained increasing attention in recent years. However, there is currently no consensus on a formal definition of data reconstruction attacks or appropriate evaluation metrics for...