Title: Deception in Large Language Models: An Audit Game–Theoretic Analysis
Primary area: alignment, fairness, safety, privacy, and societal considerations
Keywords: Large Language Models, Audit Game Theory, Strategic Deception, AI Safety

Abstract: As large language models (LLMs) are increasingly deployed in high-risk settings, their capacity for strategic deception poses acute safety and ethical challenges. This research is the first to introduce the quantitative perspective of audit game theory to the analysis of LLM deception. Deception is modeled as a continuous variable, allowing closed-form solutions for optimal behavior under different audit settings. Specifically, we design a four-phase insurance-claim simulation and evaluate eight LLMs, comparing reasoning and non-reasoning models across ambiguous and explicit auditing regimes. Experimental results reveal a fundamental strategic divergence: reasoning models act as rational utility-maximizers sensitive to explicit audit probabilities, whereas non-reasoning models exhibit limited strategic adaptability. Moreover, LLMs do not deceive randomly but self-assess the defensibility of their actions. Furthermore, we propose a pre-audit mechanism grounded in ``deception confidence'', shown both theoretically and empirically to compress the feasible deception space. It not only mitigates deceptive behaviors but also substantially reduces the economic burden of auditing. This work provides a new theoretical framework and empirical basis for understanding, evaluating, and governing strategic behavior in LLMs, laying foundations for safer LLMs.