Evaluating the social intelligence of Large Language Models (LLMs) increasingly requires moving beyond static text generation toward dynamic, adversarial interaction. We introduce the Adversarial Resource Extraction Game (AREG), a benchmark that operationalizes persuasion and resistance as a multi-turn, zero-sum negotiation over financial resources. Using a round-robin tournament across frontier models, AREG enables joint evaluation of offensive (persuasion) and defensive (resistance) capabilities within a single interactional framework. Our analysis provides evidence that these capabilities are weakly correlated ($ρ= 0.33$) and empirically dissociated: strong persuasive performance does not reliably predict strong resistance, and vice versa. Across all evaluated models, resistance scores exceed persuasion scores, indicating a systematic defensive advantage in adversarial dialogue settings. Further linguistic analysis suggests that interaction structure plays a central role in these outcomes. Incremental commitment-seeking strategies are associated with higher extraction success, while verification-seeking responses are more prevalent in successful defenses than explicit refusal. Together, these findings indicate that social influence in LLMs is not a monolithic capability and that evaluation frameworks focusing on persuasion alone may overlook asymmetric behavioral vulnerabilities.

翻译：评估大语言模型（LLMs）的社会智能日益需要从静态文本生成转向动态对抗性交互。本文提出对抗性资源提取博弈（AREG），该基准将说服与抵抗能力操作化为围绕财务资源的多轮零和谈判。通过在顶尖模型间进行循环锦标赛，AREG能够在单一交互框架内对攻击性（说服）和防御性（抵抗）能力进行联合评估。我们的分析表明这两种能力呈弱相关（$ρ= 0.33$）且在经验上相互分离：强大的说服表现并不能可靠预测其抵抗能力，反之亦然。在所有评估模型中，抵抗分数均高于说服分数，表明在对抗性对话场景中存在系统性防御优势。进一步的语言学分析表明，交互结构对这些结果起着核心作用。渐进式承诺寻求策略与更高的资源提取成功率相关，而验证寻求式回应在成功防御中的出现频率高于直接拒绝。这些发现共同表明，LLMs的社会影响力并非单一能力，仅关注说服能力的评估框架可能忽视非对称的行为脆弱性。