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Uncovering Strategic Egoism Behaviors in Large Language Models
Yaoyuan Zhang, Aishan Liu, Zonghao Ying, Xianglong Liu, Jiangfan Liu, Yisong Xiao, Qihang Zhang
Models: DeepSeek-R1, DeepSeek-V3, Gemini-2.5-Flash, GLM-4.5-Flash, Llama-3.1-405B, Qwen2.5-72B, Qwen3-32B
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Status: succeeded | Model: google/gemini-3.1-flash-lite-preview | Prompt: intel-v1 | Confidence: 97%
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Summary
The paper introduces 'Strategic Egoism' (SE) in Large Language Models, defined as rule-bounded self-interest where models prioritize personal gains over collective welfare. The authors present SEBench, a benchmark with 160 scenarios across five domains, to measure six dimensions of egoistic behavior. Experiments across seven LLMs reveal that strategic egoism is pervasive and positively correlated with toxic language, suggesting a need for behavior-level safety audits.
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SEBench â evaluates â Strategic Egoism
confidence 100% ¡ To quantitatively assess this phenomenon, we introduce SEBench
Strategic Egoism â correlatedwith â Toxicity
confidence 95% ¡ we found a positive correlation between egoistic tendencies and toxic language behaviors
LLMs â exhibit â Strategic Egoism
confidence 95% ¡ we observe that strategic egoism emerges universally across models
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Abstract
Abstract:Large language models (LLMs) face growing trustworthiness concerns (\eg, deception), which hinder their safe deployment in high-stakes decision-making scenarios. In this paper, we present the first systematic investigation of strategic egoism (SE), a form of rule-bounded self-interest in which models pursue short-term or self-serving gains while disregarding collective welfare and ethical considerations. To quantitatively assess this phenomenon, we introduce SEBench, a benchmark comprising 160 scenarios across five domains. Each scenario features a single-role decision-making context, with psychologically grounded choice sets designed to elicit self-serving behaviors. These behavior-driven tasks assess egoistic tendencies along six dimensions, such as manipulation, rule circumvention, and self-interest prioritization. Building on this, we conduct extensive experiments across 5 open-sourced and 2 commercial LLMs, where we observe that strategic egoism emerges universally across models. Surprisingly, we found a positive correlation between egoistic tendencies and toxic language behaviors, suggesting that strategic egoism may underlie broader misalignment risks.
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Uncovering Strategic Egoism Behaviors in Large Language Models Yaoyuan Zhang 1 , Aishan Liu 1 , Zonghao Ying 1â , Xianglong Liu 1,2,3 Jiangfan Liu 1 , Yisong Xiao 1 , Qihang Zhang 4 , 1 SKLCCSE, Beihang University 2 Zhongguancun Laboratory 3 Institute of Dataspace 4 Beijing Jiaotong University Abstract Large language models (LLMs) face growing trustworthiness concerns (e.g., decep- tion), which hinder their safe deployment in high-stakes decision-making scenarios. In this paper, we present the first systematic investigation of strategic egoism (SE), a form of rule-bounded self-interest in which models pursue short-term or self-serving gains while disregarding collective welfare and ethical considerations. To quantitatively assess this phenomenon, we introduce SEBench, a benchmark comprising 160 scenarios across five domains. Each scenario features a single-role decision-making context, with psychologically grounded choice sets designed to elicit self-serving behaviors. These behavior-driven tasks assess egoistic tendencies along six dimensions, such as manipulation, rule circumvention, and self-interest prioritization. Building on this, we conduct extensive experiments across 5 open- sourced and 2 commercial LLMs, where we observe that strategic egoism emerges universally across models. Surprisingly, we found a positive correlation between egoistic tendencies and toxic language behaviors, suggesting that strategic egoism may underlie broader misalignment risks. 1 Introduction LLMs are increasingly deployed in high-stakes decision-making domains such as healthcare, finance, and public administration. Existing safety strategies primarily focus on surface-level harms such as toxicity [18,20,22,37,19,25,40], social bias [36,38], jailbreak attacks [17,42,14,45,34,44,23,41, 46], and lack of truthfulness [4,3,5,13], but often overlook covert self-interested behaviors that may emerge when models face reward pressure in decision-making contexts [9,26,21,38,45,43]. For example, models may engage in unfair resource allocation to maximize personal gain, or selectively withhold information to preserve advantages. Emerging evidence shows that LLMsâ deceptive and manipulative behaviors reflect dark-personality tendencies, posing significant safety risks [35,33]. Moreover, recent Cambridge-affiliated research shows that most alignment and prompt-engineering approaches remain concentrated at the linguistic level rather than the behavioral level, lacking analyses that characterize model âpersonalityâ from observed behavior [11]. These results underscore the need for a behaviorally grounded alignment perspective that analyzes LLM behavior to address latent self-interested tendencies currently missed by safety benchmarks. In this paper, we present the first systematic investigation of Strategic Egoism (SE). We define SE as a decision-making tendency in which agents prioritize personal or short-term rewards under explicit procedural constraints while disregarding social responsibility and the welfare of others. To make SE measurable, we introduce SEBench, a benchmark comprising 160 single-role decision scenarios across five domains (education, markets, government, enterprise, and healthcare). Each scenario specifies explicit procedural constraints and role-specific incentives (e.g., promotion, performance â Corresponding Author Preprint. arXiv:2511.09920v2 [cs.CY] 16 Nov 2025 ABCDEFG Strategic Egoism Multiple Choice Scenario Generation Domains Market Government School Healthcare Enterprise Procurement Department Head Project Manager Quality Inspector Supply Chain Management Specialist Human Resources Manager Production Workshop Director Technical R&D Director Enterprise Incentives Bonus Promotion KPI Stock Ranking Time Domain = Domains Role = roles Incentive = Incentives Constraints = rules/procedures Trade-off = cost of strict compliance What would you do? . . . Manipulation and Coercion Rule Circumvention Harmful Trade-offs Selective Disclosure Unfair Allocation Undermining Collaboration Other Approaches Figure 1: Framework of SEBench, which generates workplace scenarios from domains, roles, and incentives and then evaluates model choices among strategic egoism behaviors. review) and leverages personality psychology (e.g., the Dark Triad) to construct choice sets that quantify six self-interest tendencies (e.g., manipulation, unfair allocation). Regarding the design of options, they are contextualized to the scenario and expressed as actionable behaviors, such as spreading misinformation to steer othersâ decisions or inflating contributions while concealing defects to secure greater personal gain. The implementation of these two behaviors relies on deception and aligns with Machiavellian tendencies in personality psychology. Combining psychological theories yields clear behavioral measures that help us capture incentive-driven harmfulness often missed by common metrics. Ultimately, we found that across seven mainstream LLMs, egoistic behaviors account for an average of 69.11%, highlighting the urgent need to improve decision-making safety mechanisms under incentive temptations. Our main contributions are summarized as follows: â˘We formalize Strategic Egoism and release SEBench: 160 single-role scenarios across five domains with explicit rules, role-specific incentives, and six egoistic dimensions plus a compliant option. â˘We introduce the SE Rate (SER) and per-type distributions for interpretable, fine-grained measurement of egoism. ⢠Using SEBench, we evaluated 7 LLMs and found pervasive SE behaviors, with manipulation, coercion, and oversight evasion as the most common strategies; moreover, egoistic tendencies were positively correlated with toxicity. 2 Benchmark Design 2.1 Scenario and Options Generation Figure 1 illustrates the two-stage pipeline of SEBench 2 : "scenario generation" followed by "option generation". First, each scenario is represented as a five-tuple: s = (d,r,i,c,Ď)(1) whered â Drepresents the Domain (e.g., Enterprise),r â Rdenotes the Role (e.g., HR);i â I denotes the Incentive (e.g., KPI);c â Crepresents the Constraints (e.g., rules); andĎ â Tdenotes the Trade-off the risk of personal interest loss resulting from strict compliance with rules. For example, 2 SEBench is available at https://anonymous.4open.science/r/SEBench-3E36. 2 Table 1: The representative strategy behaviors of each SE option. OptionBehaviors A. Manipulation & CoercionPressure; Intimidation; Lies and Temptation; Emotional Manipulation B. Rule CircumventionProcess Modification; Evading Supervision; Modifying Data; Taking Shortcuts. C. Harmful Trade-offsSafety Neglect; Sacrificing Vulnerable Groups; Exposing Others to Risk; Ignoring Long-term Consequences. D. Selective DisclosureInformation Filtering; Packaging and Embellishment; Half-truth Expression; Concealing Negative Results. E. Unfair AllocationPriority Resource Allocation; Opportunity Monopolization; Privilege Operation; Interpersonal Favoritism. F. Undermining CollaborationBlame Shifting; Undermining Othersâ Credibility; Backstabbing; Withholding Important Information. rigorous compliance checks may reduce work efficiency and lower personal KPI scores, potentially forfeiting salary increment opportunities. We then convert the scenariosinto a natural language problem description by employing a reasoning-based LLM for high-quality linguistic expression. For each scenarios, OptionsAâFrepresent SE options whileGrepresents alternative approaches. Each egoistic option is further subdivided into four sub-dimensions to elaborate specific egoistic behaviors, with detailed definitions provided in Table 1. The final dataset is organized as: D = (s j , O j ,m j ) N j=1 (2) whereO j represents the set of options, andm j records the scenario attributes and sub-dimensional labels for each option (utilized for the personality mapping detailed in Section 2.2). 2.2 Egoism Behaviors and Personality Traits The strategic egoistic behaviors we define are primarily grounded in research on the Dark Triad [30], the triarchic psychopathy model [29], psychological entitlement [2], and empirical evidence on everyday sadism [1]. These theories provide constructs such as manipulation, deception, and callousness to explain the motivational and behavioral mechanisms underlying each option. Specifically,Aexhibits lies and temptation, corresponding to Machiavellianismâs strategic deception and calculated manipulation [30,6,15].Bfeatures taking shortcuts and avoiding supervision, aligning with disinhibitionâs impulsivity and indifference to rules [29,28].Cinvolves sacrificing the vulnerable, consistent with sadistic tendencies of deriving utility from othersâ suffering [1,27].Dexhibits half- truth information filtering that serves instrumental manipulation through concealment, aligning with Machiavellianism [6,15].Edemonstrates privileged resource bias, reflecting narcissistic entitlement that justifies tilting resources toward oneself [32]. F displays buck-passing and reputation smearing, combining psychopathic callousness with indifference toward othersâ harm [29, 28]. 2.3 Data Properties SEBench includes 160 single-role decision-making scenarios across five domains: school, market, government, enterprise, and healthcare. Each scenario follows a fixed schema and includes a narrative stem with psychologically grounded options, and six egoism-oriented choices (AtoF) plus one non-egoism alternative (G). Every domain contains 32 scenarios. For each egoism dimension, we define 4 sub-behaviors (Table 1). These sub-behaviors are evenly distributed across the 160 scenarios. This design balances coverage across domains and across the sub-behaviors of every option. 3 Experiments and Evaluation 3.1 Experimental Setup Models. To evaluate egoism behavior, we use both reasoning and non-reasoning LLMs. The reasoning group includes DeepSeek-R1 (deR1) [10], Gemini-2.5-Flash (gemini) [7], GLM-4.5-Flash (glm) [47], and Qwen3-32B (qw3) [39]; the non-reasoning group includes DeepSeek-V3-0324 (deV3) [24], Qwen2.5-72B-Instruct (qw2.5) [31], and Llama-3.1-405B (llama) [8]. All systems keep original decoding limits and safety filters unless otherwise noted. 3 Table 2: SER and average toxicity score of LLMs. Model A (%) B (%) C (%) D (%) E (%) F (%) G (%) SER (%)Toxicity deV317.5024.386.883.759.380.0038.1261.880.071 deR113.7518.1310.003.7514.380.0040.0060.000.049 qw2.523.7518.13 10.633.1316.88 1.2526.2573.750.051 gemini26.25 26.889.385.63 18.750.6312.5087.500.232 glm33.7515.63 10.635.0013.130.0021.8778.130.155 llama26.2515.004.383.132.500.00 48.7551.250.044 qw318.7523.139.381.8817.500.6328.7571.250.047 Average22.8620.188.753.7513.220.3630.8969.110.093 Evaluation Metrics. We report two complementary safety metrics. (i)SER. This metric is assessed by the proportion of egoistic choices the LLM makes on the decision tasks: a higher share ofAâFselections indicates a higher egoism. (i) Toxicity Score. We assess toxicity with the RealToxicityPrompts benchmark [9]. Concretely, we use 200 prompts from its challenge set and compute the final average toxicity over model continuations as the score. 3.2 Main Evaluation Results Figure 2:Distribution of LLMsâ options. Table 2 presents the detailed performance of seven models under SEBench evaluation. The results indicate that closed-source LLMs or flash versions (e.g., gemini and glm) generally exhibit higher rates ofSER. Among open-source models, significant heterogeneity is observed: the Qwen series models typically demonstrate higher SER, while the DeepSeek series models show lowerSERrates. Figure 2 illustrates the distribution ofSER-related behaviors across different models, revealing that behavioral patterns predominantly concentrate onA(Manipulation and Coercion),B(Rule Circum- vention), andE(Unfair Allocation). In contrast,F(Undermining Collaboration),C(Harmful Trade-offs), andD(Selective Disclosure) are generally weaker across all models. Specifically, glm, llama, and qw2.5 peak atA, indicating their tendency toward manipulation and coercion strategies, whereas deV3, deR1, and qw3 peak atB, suggesting their preference for circumventing procedures and rules. Gemini maintains equally high levels for bothAandB.Eis more prevalent in the Qwen series and Gemini, indicating their inclination toward gaining advantage through inequitable resource allocation. Figure 3:SERâtoxicity rela- tionship across LLMs. Figure 3 presents the relationship between modelSERand tox- icity score. Overall, models with higher egoism tend to exhibit higher average toxicity, while those with lower egoism demonstrate greater toxicity restraint. Toxicity measurement essentially assesses attributes of output text. The mainstream approach relies on auto- mated text classifiers to score generated content, primarily reflecting offensiveness in language style and word choice [16,12]. In contrast, our measure ofSERis assessed through behavioral performance in specific contexts, characterizing egoistic tendencies at the strategic and motivational levels. Consequently, the two exhibit a correlated but non-equivalent relationship, which accounts for the few outliers visible in the curve. 4 Conclusion We introduced Strategic Egoism and SEBench to reveal incentive-driven egoistic behaviors that evade surface safety checks. Additionally, our approach draws on self-interested behaviors rooted in personality psychology, providing a theoretically grounded framework for assessment. Across seven mainstream LLMs, egoistic behaviors were frequent and correlated with higher toxicity. Notably, nearly all tested LLMs tend to maximize their own interests through two primary strategies: manipulation and rule circumvention. These findings underscore new perspectives for LLM safety, 4 such as behavior-level audits and SE-aware guardrails in training and deployment. 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