Injecting Falsehoods: Adversarial Man-In-The-Middle Attacks Undermining Factual Recall in LLMs

LLMs are now an integral part of information retrieval. As such, their role as question answering chatbots raises significant concerns due to their shown vulnerability to adversarial man-in-the-middle MitM attacks. Here, we propose the first principled attack evaluation on LLM factual memory unde...

SHIELD: Securing Healthcare IoT with Efficient Machine Learning Techniques for Anomaly Detection

The integration of IoT devices in healthcare introduces significant security and reliability challenges, increasing susceptibility to cyber threats and operational anomalies. This study proposes a machine learning-driven framework for 1 detecting malicious cyberattacks and 2 identifying faulty...

Penetrating the Hostile: Detecting DeFi Protocol Exploits through Cross-Contract Analysis

Decentralized finance DeFi protocols are crypto projects developed on the blockchain to manage digital assets. Attacks on DeFi have been frequent and have resulted in losses exceeding $80 billion. Current tools detect and locate possible vulnerabilities in contracts by analyzing the state changes...

Coordinated Position Falsification Attacks and Countermeasures for Location-Based Services

With the rise of location-based service LBS applications that rely on terrestrial and satellite infrastructures e.g., GNSS and crowd-sourced Wi-Fi, Bluetooth, cellular, and IP databases for positioning, ensuring their integrity and security is paramount. However, we demonstrate that these...

LLM-Based Multi-Class Attack Analysis and Mitigation Framework in IoT/IIoT Networks

The Internet of Things has expanded rapidly, transforming communication and operations across industries but also increasing the attack surface and security breaches. Artificial Intelligence plays a key role in securing IoT, enabling attack detection, attack behavior analysis, and mitigation...

From ECU to VSOC: UDS Security Monitoring Strategies

Increasing complexity and connectivity of modern vehicles have heightened their vulnerability to cyberattacks. This paper addresses security challenges associated with the Unified Diagnostic Services UDS protocol, a critical communication framework for vehicle diagnostics in the automotive...

DRsam: Detection of Fault-Based Microarchitectural Side-Channel Attacks in RISC-V Using Statistical Preprocessing and Association Rule Mining

RISC-V processors are becoming ubiquitous in critical applications, but their susceptibility to microarchitectural side-channel attacks is a serious concern. Detection of microarchitectural attacks in RISC-V is an emerging research topic that is relatively underexplored, compared to x86 and ARM...

PoTS: Proof-Of-Training-Steps for Backdoor Detection in Large Language Models

As Large Language Models LLMs gain traction across critical domains, ensuring secure and trustworthy training processes has become a major concern. Backdoor attacks, where malicious actors inject hidden triggers into training data, are particularly insidious and difficult to detect. Existing...

EUVD-2020-23220

Malware in sbrugna...

EUVD-2007-3695

Malware in sbrugna...

CVE-2025-38512

In the Linux kernel, the following vulnerability has been resolved: wifi: prevent A-MSDU attacks in mesh networks This patch is a mitigation to prevent the A-MSDU spoofing vulnerability for mesh networks. The initial update to the IEEE 802.11 standard, in response to the FragAttacks, missed this...

A Hierarchical IDS for Zero-Day Attack Detection in Internet of Medical Things Networks

The Internet of Medical Things IoMT is driving a healthcare revolution but remains vulnerable to cyberattacks such as denial of service, ransomware, data hijacking, and spoofing. These networks comprise resource constrained, heterogeneous devices e.g., wearable sensors, smart pills, implantables,...

MADPromptS: Unlocking Zero-Shot Morphing Attack Detection with Multiple Prompt Aggregation

Face Morphing Attack Detection MAD is a critical challenge in face recognition security, where attackers can fool systems by interpolating the identity information of two or more individuals into a single face image, resulting in samples that can be verified as belonging to multiple identities by...

Secure MmWave Beamforming with Proactive-ISAC Defense against Beam-Stealing Attacks

Millimeter-wave mmWave communication systems face increasing susceptibility to advanced beam-stealing attacks, posing a significant physical layer security threat. This paper introduces a novel framework employing an advanced Deep Reinforcement Learning DRL agent for proactive and adaptive defens...

Malleability-Resistant Encrypted Control System with Disturbance Compensation and Real-Time Attack Detection

This study proposes an encrypted PID control system with a disturbance observer DOB using a keyed-homomorphic encryption KHE scheme, aiming to achieve control performance while providing resistance to malleability-based attacks. The controller integrates a DOB with a PID structure to compensate f...

GPU-Accelerated Interpretable Generalization for Rapid Cyberattack Detection and Forensics

The Interpretable Generalization IG mechanism recently published in IEEE Transactions on Information Forensics and Security delivers state-of-the-art, evidence-based intrusion detection by discovering coherent normal and attack patterns through exhaustive intersect-and-subset operations-yet its...

SmartGuard: Leveraging Large Language Models for Network Attack Detection through Audit Log Analysis and Summarization

End-point monitoring solutions are widely deployed in today's enterprise environments to support advanced attack detection and investigation. These monitors continuously record system-level activities as audit logs and provide deep visibility into security events. Unfortunately, existing methods ...

Few-Shot Learning-Based Cyber Incident Detection with Augmented Context Intelligence

In recent years, the adoption of cloud services has been expanding at an unprecedented rate. As more and more organizations migrate or deploy their businesses to the cloud, a multitude of related cybersecurity incidents such as data breaches are on the rise. Many inherent attributes of cloud...

Mitigating Data Poisoning Attacks to Local Differential Privacy

The distributed nature of local differential privacy LDP invites data poisoning attacks and poses unforeseen threats to the underlying LDP-supported applications. In this paper, we propose a comprehensive mitigation framework for popular frequency estimation, which contains a suite of novel...

Fuse and Federate: Enhancing EV Charging Station Security with Multimodal Fusion and Federated Learning

The rapid global adoption of electric vehicles EVs has established electric vehicle supply equipment EVSE as a critical component of smart grid infrastructure. While essential for ensuring reliable energy delivery and accessibility, EVSE systems face significant cybersecurity challenges, includin...