Psychological constructs in humans range along a state–trait continuum: traits persist across situations, while states fluctuate with context. Studies have shown that language models exhibit measurable psychological constructs, yet whether these constructs differ in contextual stability, as the state–trait distinction predicts, remains untested. We present the Questionnaire for Causal Language Models (QCLM), a psychometric framework that measures constructs through next-token probability distributions of base models. Applying QCLM to 35 causal language models under vanilla, stress, and neutral conditions, we assess two anxiety instruments targeting opposite ends of the state–trait continuum: STAI-S (state anxiety) and STAI-T (trait anxiety). Paired effect sizes and variance decomposition reveal that state anxiety is more sensitive to stress manipulation than trait anxiety: stimulus type accounts for a larger share of variance in state anxiety, while model identity contributes more to trait anxiety. These results provide empirical evidence that the state–trait distinction extends to language model behavior.

LLM-based assistants have become ubiquitous, yet information security awareness (ISA) remains an underexplored aspect of LLM safety. ISA encompasses not only security knowledge, but also the attitudes and behaviors required to recognize implicit security risks and avoid prioritizing user satisfaction over secure guidance. We introduce LISAA, a framework for evaluating LLM ISA under realistic end-user interactions. The framework includes a dataset of 100 realistic scenarios spanning all criteria of an ISA taxonomy, each with a tension between implicit security implications and user satisfaction. We further introduce methods for systematically refining realistic ISA scenarios from authentic user queries and for selecting reliable LLM judges without requiring large annotated datasets. Robustness analyses show that LISAA rankings remain stable under scenario resampling and stochastic generation, while construct-validity experiments demonstrate sensitivity to changes in security behavior. Applying LISAA to leading LLMs reveals that many widely deployed models still exhibit medium-to-low ISA levels, and that strong performance on traditional cybersecurity knowledge benchmarks does not necessarily translate to high ISA. In addition, the framework enables comparative analyses across model families, revealing consistent trends such as higher ISA in newer and larger variants, although meaningful gaps persist even among recent models. We release the dataset, evaluation pipeline, source code, and an online tool for evaluating new models using the LISAA framework.

Human-like personality traits have recently been discovered in large language models, raising the hypothesis that their (known and as yet undiscovered) biases conform with human latent psychological constructs. While large conversational models may be tricked into answering psychometric questionnaires, the latent psychological constructs of thousands of simpler transformers, trained for other tasks, cannot be assessed because appropriate psychometric methods are currently lacking. Here, we show how standard psychological questionnaires can be reformulated into natural language inference prompts, and we provide a code library to support the psychometric assessment of arbitrary models. We demonstrate, using a sample of 88 publicly available models, the existence of human-like mental health-related constructs—including anxiety, depression, and the sense of coherence—which conform with standard theories in human psychology and show similar correlations and mitigation strategies. The ability to interpret and rectify the performance of language models by using psychological tools can boost the development of more explainable, controllable, and trustworthy models.