The paper argues that despite the focus on risk, the cybersecurity profession is structurally trained as a threat-management discipline, leading to poor foundational risk reasoning among professionals…

The paper analyzes the CIIM risk model using postphenomenology, arguing that such formal models act as mediating artifacts that fundamentally shape how cybersecurity practitioners perceive and respond…

This study evaluated a personality-conditional cybersecurity training system, TailoredSec, finding that routing content based on a user's Five-Factor Model (FFM) trait significantly improved post-trai…

The paper introduces CyberCertBench, a new benchmark suite for evaluating LLMs against industry cybersecurity certifications, finding that while frontier models perform well on general knowledge, thei…

This study demonstrates that an LLM's assigned support role (e.g., Inform, Coach, Relate) significantly alters its safety profile and the types of risks it presents when assisting users in complex car…

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 study compared the cybersecurity risk assessment capabilities of five popular large language models (LLMs) against human experts, finding that LLMs consistently underestimated risks and require ma…

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…

The paper introduces SecLens-R, a multi-stakeholder evaluation framework, demonstrating that LLM performance for vulnerability detection varies significantly depending on the specific priorities (e.g.…

This paper analyzes how a financial-technology organization operationalizes the ISO/IEC 27001:2022 standard by examining eight core security procedures, concluding that an effective ISMS requires a ti…

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 introduces SKILLSCOPE, a system that detects security-relevant behaviors in code-backed LLM skills that are not disclosed in the natural language description, finding that 9.4% of skills exh…

The study found that providing developers with a layer-based security training package significantly reduces the number and severity of security vulnerabilities in LLM-assisted web application develop…

This study empirically demonstrates that even highly technical students struggle significantly with the long-term usability and security understanding of Mutual TLS (mTLS) client authentication, sugge…

The study evaluated text-based explanations of Trusted Execution Environments (TEEs) to non-experts, finding that while non-technical explanations improved understanding, they did not significantly in…

The paper introduces CAESAR, a novel multi-agent framework that coordinates LLM agents across five specialized roles to improve success rates and stability in complex, multi-stage cyber intrusion task…

This experience report details the process and developer perceptions of integrating log-based fraud detection into an Agile workflow, providing practical best practices for embedding security analytic…

LLM-FACETS introduces an open-source, privacy-preserving framework designed to enable non-technical domain experts and compliance officers to audit and evaluate the transparency and accountability of…

The paper investigates how LLM agents determine the security of their execution environment in a simulated negotiation setting, finding that while they can detect danger, they cannot reliably verify s…

The paper introduces a validated, consensus-labeled prompt bank that separates requests for executable malicious code (weapons) from requests for general harmful security knowledge, providing a more g…