Lighting the Dark Side of the Model: Psychometric Probing of Dark Triad Responses in LLMs

Background: Large language models (LLMs) are increasingly deployed in socially sensitive contexts, raising concerns about how their outputs reflect or reproduce socially aversive response patterns. Prior research has focused on socially desirable traits (e.g., the Big Five), while less attention has been given to the Dark Triad—Machiavellianism, narcissism, and psychopathy. Methods: This exploratory study applies a psychometric probing approach using the Short Dark Triad (SD3) as a structured questionnaire to analyze response patterns in 12 LLMs. Through controlled persona prompting (gender, age, religion), more than 540,000 responses were generated across model–persona–item observations. Outputs were scored using standard psychometric procedures and compared with pooled human SD3 reference values using Hedges’ g as an interpretive benchmark. Results: Across models, Machiavellianism-related scores tend to be lower than human references, narcissism-related scores slightly higher, and psychopathy-related scores largely within the human range. Persona prompting produces systematic but uneven effects: gender has minimal impact, age yields small shifts, and religious framing leads to the strongest and most consistent decreases in socially aversive response patterns. However, observed differences showed notable heterogeneity across models. Conclusions: The findings should be understood as response patterns shaped by context and specific prompting conditions. Our findings suggest that persona effects reflect normative activation, not identity simulation. Persona prompts seem to trigger patterns in the training data rather than consistent, identity-specific traits. Overall, the study highlights both the potential and limitations of psychometric-style probing for evaluating LLM behavior and underscores the importance of prompt context in shaping model outputs.