AREG: Adversarial Resource Extraction Game for Evaluating Persuasion and Resistance in Large Language Models

Evaluating LLM social intelligence requires moving beyond static text toward dynamic interactions. We introduce the Adversarial Resource Extraction Game (AREG), a benchmark operationalizing persuasion and resistance as a multi-turn, zero-sum financial negotiation. A tournament across frontier models reveals that offensive and defensive capabilities are empirically dissociated and weakly correlated ($\rho = 0.33$). While models show a systematic defensive advantage, effectiveness depends heavily on dialogue structure: incremental persuasion outperforms single asks, and verification-seeking defends better than explicit refusal. These findings demonstrate that social influence is not a monolithic capability, highlighting the need for dual-sided evaluation to uncover asymmetric behavioral vulnerabilities.