This study investigates the manifestation of Schwartz’s Theory of Basic Human Values (STBV) within a pre-trained Large Language Model (LLM) by evaluating how persona prompting influences the model’s responses. Specifically, we use prompt-based persona induction to represent Schwartz’s ten broad value types e.g., Universalism, Achievement) and measure their effects using the model’s responses to the Portrait Values Questionnaire Revised (PVQ-RR). A neutral persona and human baseline serve as control conditions to assess the influence of persona prompting. Prompting strategies are also compared. Results show that value-based persona prompts systematically shift the model’s PVQ-RR response profiles, indicating that LLM questionnaire responses can be steered along Schwartz value dimensions under controlled prompting conditions. These findings suggest that value-based persona prompts may be useful for studying and configuring the expressed response profiles of LLM-based agents. Using Schwartz’s values as a structured measurement framework provides a way to evaluate how LLM responses change under persona prompting and offers a basis for future studies of value-conditioned agent behaviour.

This paper presents the SANDMAN architecture for cyber deception, employing Language Agents to create convincing human simulacra. These “Deceptive Agents” serve as advanced cyber decoys, designed to engage attackers to extend the observation period of attack behaviours. This research demonstrates the viability of persona-driven Deceptive Agents to generate plausible human activity to enhance the effectiveness of cyber deception strategies. Through experimentation, measurement and analysis, we illustrate how a prompt schema induces specific “personalities”, defined by the five-factor model of personality, in Large Language Models to generate measurably diverse, and plausible, behaviours.