Uncovering Strategic Egoism Behaviors in Large Language Models

摘要

Large language models (LLMs) exhibit growing safety and alignment risks, hindering their deployment in high-stakes decision-making scenarios. In this paper, we identify a previously underexplored risk: similar to humans, LLMs can exhibit egoistic decision-making, in which they pursue short-term self-benefits through improper means while disregarding collective welfare and ethical constraints. We term this phenomenon Strategic Egoism (SE). To systematically evaluate SE, we introduce SEBench, a benchmark comprising 880 decision-making scenarios across 11 domains involving explicit profit temptations, which measures egoistic behavior along 6 psychologically grounded dimensions (e.g., rule circumvention). Each scenario adopts a single-role decision-making setting with carefully designed choice options to elicit self-serving strategies. Extensive experiments on 9 proprietary LLMs reveal that SE behaviors are widespread, with an average occurrence rate of 67.96%, and frequently manifest as manipulative coercion. Notably, we find that models more susceptible to profit temptations also exhibit broader safety deficiencies, including higher toxicity, lower truthfulness, increased jailbreak vulnerability, and elevated Dark Triad–style trait scores. Drawing inspiration from psychological interventions, we further propose SEGuard, a lightweight mitigation that reinforces situational constraints and suppresses egoistic tactics.