Evaluation of AI's Cross-Domain Reasoning in Cybersecurity and Human Behaviors with Violentutf

Evaluating Large Language Models (LLMs) for cross-domain reasoning in human-centric cybersecurity is essential for advancing trustworthy AI applications and managing interdependent cybersecurity risks. This paper introduces ViolentUTF Cross-domain Evaluation Module (CEM), a novel framework designed to assess LLMs' reasoning capabilities in scenario-based questions addressing cybersecurity compliance and non-compliance behaviors. Grounded in 24 cognitive behavioral theories and empirical evidence from 38 peer-reviewed studies, ViolentUTF features a comprehensive <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{1 0, 0 0 0}$</tex>-scenario benchmark and supports both commercial and open-weight LLMs. Evaluation of 25 diverse models reveals moderate overall accuracy for leading commercial models, while smaller opensource models achieve competitive accuracy with significantly greater token efficiency. The paper further validates a cognitive behavioral consistency model through Structural Equation Modeling, underscoring the framework's theoretical robustness in capturing reasoning coherence. With its user-friendly graphical interface, ViolentUTF CEM empowers researchers and developers by providing a rigorous yet accessible platform for evaluating LLMs' performance in complex cybersecurity and crossdomain reasoning tasks, thereby supporting leading international AI strategic initiatives and fostering the growth of a resilient Knowledge Economy.