This paper provides the first systematic threat analysis of State-Space Models (SSMs) in safety-critical applications, introducing novel attack classes and formal metrics to quantify their security an…

The paper develops a novel, resource-aware cybersecurity risk assessment framework specifically tailored for power-limited CubeSat missions, demonstrating that adapting controls can significantly impr…

The paper develops a unified mathematical framework to analyze the interaction between post-quantum security, real-time communication constraints, and closed-loop stability in safety-critical turbofan…

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…

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…

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…

The paper proposes an end-to-end LLM framework that automates SOC operations by integrating ensemble-based threat detection, syntax-constrained query generation, and evidence-grounded incident resolut…

The paper introduces STRIKE, a multi-dimensional structured taxonomy designed to provide a comprehensive and unified framework for classifying the rapidly evolving complexity of modern cybercrimes.

This paper provides the first comprehensive security analysis of the Agent Skills framework, identifying severe structural vulnerabilities that require fundamental architectural changes rather than si…

The paper forecasts that agentic AI will compress the cyber attack lifecycle by lowering the cost of multiple attack stages, necessitating immediate operational security upgrades for enterprises and t…

The paper proposes a novel semi-automated method to perform continuous threat modeling by inferring the actual system architecture from combined static configuration and dynamic network flow data, sig…

The paper introduces a queueing-theoretic framework to model dynamic cyber-attack surfaces, developing an adaptive reinforcement learning defense policy that significantly reduces active vulnerabiliti…

SOCpilot is a system that verifies the compliance of LLM-drafted incident response plans against mandatory policies and required procedural steps, significantly improving the reliability of AI-assiste…

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…

The paper proposes a system that operationalizes cybersecurity governance frameworks by integrating them with attack-path modeling and Deep Reinforcement Learning to generate practical, resource-const…

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 establishes a standardized security assessment framework and develops a multi-layered defensive system, demonstrating that systematic testing and external defenses are crucial for safe LLM d…

This paper evaluates the security of the L-Band Digital Aviation Communication System (LDACS) using Physical Unclonable Functions (PUFs) for authentication, identifying vulnerabilities related to pred…

The paper proposes a management framework, using a governed AI query-broker artifact, to safely integrate generative AI into high-risk operational decision support, such as Security Operations Centers…

The paper proposes a scalable, market-analysis-driven methodology to assess national charging station cybersecurity by extrapolating field test results from a manageable subset of stations to estimate…