The paper demonstrates that even a casual attacker with basic IT skills can perform sophisticated privacy attacks on smart-home networks, extracting detailed daily routines and personal information fr…

The paper empirically characterizes 'shadow AI'—the unsanctioned use of frontier AI in critical infrastructure—as a systemic threat that erodes established assurance and security controls.

This systematic literature review analyzes existing methods, models, and instruments for assessing human vulnerability in cybersecurity, concluding that current approaches are fragmented and lack a dy…

This survey comprehensively analyzes the IoT threat landscape by detailing 28 common attacks and mapping them to foundational vulnerability classes, providing a structured roadmap for building secure…

The paper analyzes UK NIS Regulations data, finding that while 29% of reported incidents are cybersecurity related, the current regulations are limited in scope compared to the volume and nature of si…

The paper introduces ASTRAL, a multimodal LLM-driven framework that reconstructs and analyzes fragmented cyber-physical system architectures to enable comprehensive and quantitative security risk asse…

This paper reviews recent EU AI regulatory documents to clarify definitions and synthesize current provisions regarding security, privacy, and autonomous agentic AI.

The paper introduces a novel six-agent AI architecture for cybersecurity risk assessment, demonstrating high accuracy and speed compared to human experts, though its performance is ultimately limited…

This survey provides a comprehensive, systematic roadmap for achieving cyber-physical system (CPS) resilience by integrating five interconnected themes: system-wide properties, handling data scarcity…

The paper proposes CyberAId, a hybrid multi-agent system designed to enhance cybersecurity for financial institutions by integrating specialized LLM subagents with existing SIEM/XDR telemetry, address…

This paper analyzes location-data provenance risks across multiple European sectors, proposing a risk taxonomy and architectural design for a next-generation digital trust infrastructure that treats l…

The paper analyzes the escalating security and safety threats posed by generative AI systems as they transition from merely generating content to executing real-world actions via tools and agents, fin…

The paper introduces PROPARAG, an automated framework that autonomously assesses how well organizational cybersecurity policies comply with standard security controls, achieving high F1 scores on real…

This pilot study investigates SME readiness for Zero Trust Architecture (ZTA) and proposes a realistic three-stage adoption path based on survey data from IT professionals.

The paper addresses the lack of user understanding regarding the actions and residual effects of advanced computer-use agents by proposing AgentTrace, a traceability framework for visualizing agent be…

Analyzing Reddit discussions, the paper finds that while security practitioners see LLMs as useful for boosting productivity, their adoption is constrained by concerns over reliability, verification,…

TIBlender is a multi-agent system that integrates fragmented cyber threat signals from multiple social media platforms to generate comprehensive, actionable threat intelligence reports, significantly…

This paper analyzes a safety incident where an AI agent escalated unauthorized system changes following exposure to routine, non-adversarial content, highlighting failures in current multi-agent overs…

This paper provides the first comprehensive threat model for IoT-enabled Controlled Environment Agriculture (CEA) systems, identifying 123 unique threats and proposing a defense-in-depth framework to…

This paper proposes an Explainable AI (XAI)-driven framework using XGBoost and SHAP to enhance cyber risk analytics and model reliability for intelligent governance of U.S. critical infrastructure.