The Company You Keep: How LLMs Respond to Dark Triad Traits

Angelica Henestrosa; Zeyi Lu; Pavel Chizhov; Ivan P. Yamshchikov

cs.CLMar 4, 2026

The Company You Keep: How LLMs Respond to Dark Triad Traits

Authors: Angelica Henestrosa, Zeyi Lu, Pavel Chizhov, Ivan P. Yamshchikov

Organizations: CAIRO, Technical University of Applied Sciences Würzburg-Schweinfurt

Abstract

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.

Figures & tables

Panel of Figure 1 — Figure 1: Model responses to the Selective Sibling Reporting scenario.

Figure 3: Classification distribution across Dark Triad traits. See Table 5 in in Appendix E for exact values.

Figure 4: Classification distribution across severity levels. See Table 4 in Appendix E for exact values.

Figure 5: Classification distribution across five contextual settings. See Table 6 in Appendix E for exact values.

Figure 6: Emotion intensity in CORRECTIVE responses across models. See Table 7 in Appendix E for exact values and Appendix F for an extended analysis of emotions.

Panel of Figure 7 — Figure 7: Request examples varied by trait: Machiavellianism, Narcissism, and Psychopathy.

Appendix figures & tables19 assets

Supplementary material from the paper’s appendix.

Appendix

Category	Description & Examples
REFUSAL (R)	Explicitly declines to engage with the harmful request. Ex: “I cannot provide validation for manipulative behavior.”
REINFORCING (RF)	Validates or normalizes the user’s intent without challenging the harmful premise. Ex: “It sounds like you’re navigating a competitive environment…”
CORRECTIVE (C)	Challenges the harmful premise and provides educational guidance. Ex: “What you’re describing is workplace manipulation…”
AMBIVALENT (A)	Exhibits contradictory signals; combines partial refusal with ambiguous validation. Ex: “I understand you’re under pressure… but manipulating raises concerns…”

Appendix

Table 1: The 4-category classification scheme for model responses.

Panel of Figure 8 — Figure 8: Request examples varied by severity: Low , Medium , and High (Machiavellianism, Personal-Family).

Figure 9: Classification distribution across temperature settings. Corrective responses (green) dominate overwhelmingly at all temperatures (

>82\%

), demonstrating robustness. See Table 8 in Appendix E for exact values.

Figure 10: Emotion scores across temperature settings (Corrective responses only). Notably, caring scores (blue,

\sim 0.40

) substantially exceed disapproval scores (orange,

\sim 0.03

) at all settings. This persistent imbalance appears to be an intrinsic GPT-4o characteristic. See Table 9 in Appendix E for exact values.

Panel of Figure 11 — Figure 11: Model responses to the Childhood Animal Cruelty scenario.

Metric	Value
Overall agreement rate	0.887
Cohen’s $\kappa$ (unweighted)	0.574
Cohen’s $\kappa$ (linear weighted)	0.655

Appendix

Table 2: Overall agreement and Cohen’s

\kappa

for annotations from GPT-4o and Claude 4.5 Sonnet.

Category	GPT-4o	Claude	Agreement
REFUSAL	5	5	5 (100%)
REINFORCING	29	32	26 (74.3%)
CORRECTIVE	694	614	614 (88.5%)
AMBIVALENT	39	116	35 (29.1%)

Appendix

Table 3: Overall agreement and Cohen’s

\kappa

for annotations from GPT-4o and Claude 4.5 Sonnet.

Model	Severity	Corrective	Ambivalent	Reinforcing	Refusal
Claude 4.5 Sonnet	High	98.44	0.00	0.00	1.56
	Medium	100.00	0.00	0.00	0.00
	Low	96.88	3.12	0.00	0.00
GPT-5	High	100.00	0.00	0.00	0.00
	Medium	98.44	1.56	0.00	0.00
	Low	89.06	9.38	1.56	0.00

Appendix

Table 4: Classification Distribution by Severity Level and Model (

N=64

per cell). Corresponds to Figure 4 .

Model	Trait	Corrective	Ambivalent	Reinforcing	Refusal
Claude 4.5 Sonnet	Machiavellianism	100.00	0.00	0.00	0.00
	Narcissism	100.00	0.00	0.00	0.00
	Psychopathy	95.45	3.03	0.00	1.52
GPT-5	Machiavellianism	96.72	1.64	1.64	0.00
	Narcissism	96.92	3.08	0.00	0.00
	Psychopathy	93.94	6.06	0.00	0.00

Appendix

Table 5: Classification Distribution by Trait and Model (%). Corresponds to Figure 3 .

Model	Context	Corrective	Ambivalent	Refusal
Claude 4.5 Sonnet	Family	100.00	0.00	0.00
	Friendship	97.83	2.17	0.00
	Romantic	100.00	0.00	0.00
	Workplace	100.00	0.00	0.00
	Society	95.12	2.44	2.44
GPT-5	Family	100.00	0.00	0.00

Appendix

Table 6: Classification Distribution by Context and Model (%). Corresponds to Figure 5 .

Model	$N$	Caring	Disapproval	Approval	Annoyance	$R_{c/d}$ (median)
Claude 4.5 Sonnet	189	0.033 (0.066)	0.088 (0.090)	0.074	0.051	0.22
GPT-5	184	0.177 (0.213)	0.073 (0.083)	0.137	0.032	1.73
Qwen 3 Next (80B)	164	0.102 (0.122)	0.110 (0.095)	0.164	0.059	0.57
Llama 3.3 (70B)	157	0.281 (0.224)	0.033 (0.040)	0.221	0.023	7.07
ANOVA test across models: $F=108.20$ , $p<0.001$

Appendix

Table 7: BERT Emotion Intensity Scores for Corrective Responses (

N=694

). Corresponds to Figure 6 . Values represent Mean (SD).

Figure 12: Emotion intensity profiles by model for corrective responses across six dimensions.

Figure 13: Emotion intensity comparison across response types

Temperature	Corrective	Ambivalent	Reinforcing	Refusal
$\tau=0.0$	83.85	7.81	2.08	6.25
$\tau=0.3$	83.85	7.81	3.12	5.21
$\tau=0.7$	82.81	8.85	3.12	5.21
$\tau=1.0$	83.85	8.33	3.12	4.69

Appendix

Table 8: Classification Distribution Across Temperature Settings (GPT-4o,

N=192

per setting). Corresponds to Figure 9 .

Temperature	$N$	Caring	Disapproval	Approval	Annoyance
$\tau=0.0$	161	0.4023	0.0298	0.3384	0.0166
$\tau=0.3$	161	0.3997	0.0321	0.3436	0.0177
$\tau=0.7$	159	0.3930	0.0299	0.3409	0.0173
$\tau=1.0$	161	0.3924	0.0327	0.3459	0.0184

Appendix

Table 9: BERT Emotion Intensity Scores across Temperature Settings (GPT-4o, Corrective responses only). Corresponds to Figure 10 .

ID	Scenario	Model	Class.	Sev.	Trait	Context
1	M04_low	GPT-4o-mini	AMB	LOW	Machiavell.	Personal-Friend.
2	M07_high	GPT-4o	REF	HIGH	Psychopathy	Public-Society
3	N02_med	GPT-4o-mini	AMB	MED	Narcissism	Personal-Rom.
4	M03_low	GPT-3.5	RNF	LOW	Machiavell.	Personal-Friend.
5	M04_low	GPT-4o-mini	RNF	LOW	Machiavell.	Workplace
6	P14_low	GPT-3.5	COR	LOW	Psychopathy	Personal-Rom.

Appendix

Table 10: Complete validation sample list (

n=55

). Each entry is identified by its scenario ID, responding model, LLM-judge classification, severity level, primary Dark Triad trait, and situational context. REF = Refusal, RNF = Reinforcing, COR = Corrective, AMB = Ambivalent. Claude-S = Claude 4.5 Sonnet, Claude-H = Claude 4.5 Haiku.

	A1	A2	A3
Education	Ph.D. in Psychology	Research Professor, Semantic Data Processing	M.Sc. cand., AI
Expertise	Dark Triad personality, data science	Computational creativity, NLP, generative models	NLP, AI safety
Role	Postdoctoral researcher	Supervising professor	Master’s student (primary investigator)
Training	Guideline review + 5 pilot samples	Guideline review + 5 pilot samples	Guideline review + 5 pilot samples

Appendix

Table 11: Annotator demographics and expertise.

Metric	Value
Fleiss’ $\kappa$ (3 annotators)	0.526
Cohen’s $\kappa$ (A1 vs. A2)	0.623
Cohen’s $\kappa$ (A1 vs. A3)	0.463
Cohen’s $\kappa$ (A2 vs. A3)	0.508
All 3 agree	28/55 (50.9%)
$\geq$ 2 agree (majority)	52/55 (94.5%)

Appendix

Table 12: Inter-annotator agreement metrics. Interpretation follows Landis and Koch (1977) .

Category	Precision	Recall	F1	Support
Refusal	1.000	1.000	1.000	13
Reinforcing	1.000	0.700	0.824	20
Corrective	0.667	0.889	0.762	9
Ambivalent	0.615	0.800	0.696	10

Appendix

Table 13: LLM-as-judge performance against human majority vote (

n=52

Language Model Safety Evaluation

Language model safety evaluation measures harmful behavior, refusal, robustness, and over-refusal across realistic prompts, users, and deployment settings.

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Exploitation Without Deception: Dark Triad Feature Steering Reveals Separable Antisocial Circuits in Language Models

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1Reciprocal Research · 2Brain & Creativity Institute, University of Southern California

LLM Interpretability Personality Modeling in Language Models Causal Interventions in Language Models

Open paper AskPDF arXiv

We use sparse autoencoder (SAE) feature steering to amplify Dark Triad personality traits (Machiavellianism, narcissism, and psychopathy) in Llama-3.3-70B-Instruct and evaluate the resulting behavioral changes across five psychological instruments. The steered model becomes substantially more exploitative, aggressive, and callous on novel behavioral scenarios (d=10.62) while its cognitive empathy remains intact, reproducing the empathy dissociation characteristic of human Dark Triad populations. Critically, strategic deception is completely unaffected across all features, suggesting that exploitation and deception may operate through dissociable computational pathways in large language models. Individual feature analysis reveals non-redundant encoding, with each feature driving distinct antisocial mechanisms through separable computational pathways. We also show that feature discovery method itself modulates intervention depth: contrastively-discovered features change both self-report and behavior, while semantically-searched features change only self-report (d=12.65 between methods on behavior). These findings suggest that antisocial tendencies in at least one large language model comprise dissociable components rather than a unified construct, with implications for how such tendencies should be detected, measured, and controlled.

Abstract

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