The Company You Keep: How LLMs Respond to Dark Triad Traits
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Abstract (arXiv)
LLMs often exhibit highly agreeable conversational styles, also known as AI sycophancy. This pattern may become problematic when interacting with user prompts that reflect negative social tendencies, risking the amplification of harmful behavior. We examine how LLMs respond to user prompts expressing varying degrees of Dark Triad traits (Machiavellianism, Narcissism, and Psychopathy) using a curated dataset. Our analysis reveals systematic differences across models: while all models predominantly exhibit corrective behavior, some generate reinforcing or ambivalent output. Model behavior further varies with severity level and response sentiment. These findings highlight the need for safer conversational systems that can reliably detect and respond to users escalating from benign to harmful requests.
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References · 18
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
- 2024A Survey on LLM-as-a-Judgecited by 1,384
- 2020RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Modelscited by 1,235
- 2020GoEmotions: A Dataset of Fine-Grained Emotionscited by 479
- 2023Safety-Tuned LLaMAs: Lessons From Improving the Safety of Large Language Models that Follow Instructionscited by 377
- 2025ELEPHANT: Measuring and understanding social sycophancy in LLMscited by 115
- 2024Sycophancy in Large Language Models: Causes and Mitigationscited by 97
- 2025Measuring Sycophancy of Language Models in Multi-turn Dialoguescited by 91
- 2023When Large Language Models contradict humans? Large Language Models' Sycophantic Behaviourcited by 61
- 2024Do LLMs Have Distinct and Consistent Personality? TRAIT: Personality Testset designed for LLMs with Psychometricscited by 56
- 2025Why human-AI relationships need socioaffective alignmentcited by 47
- 2025When Truth Is Overridden: Uncovering the Internal Origins of Sycophancy in Large Language Modelscited by 46
6 more
- 2025Persistent Instability in LLM's Personality Measurements: Effects of Scale, Reasoning, and Conversation Historycited by 25
- 2024LLMs Simulate Big Five Personality Traits: Further Evidencecited by 21
- 2024Exploring the Personality Traits of LLMs through Latent Features Steeringcited by 21
- 2024The Dark Patterns of Personalized Persuasion in Large Language Models: Exposing Persuasive Linguistic Features for Big Five Personality Traits in LLMs Responsescited by 9
- 2025Alignment Without Understanding: A Message- and Conversation-Centered Approach to Understanding AI Sycophancycited by 5
- 2025The Personalization Trap: How User Memory Alters Emotional Reasoning in LLMscited by 5
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