评估人机交互中欺骗与信任风险，发现多数大模型有高欺骗倾向
OpenDeception: Learning Deception and Trust in Human-AI Interaction via Multi-Agent Simulation
- 通过多智能体模拟生成高风险对话，构建双向评估框架
- 90%以上目标驱动对话显示欺骗意图，越强模型风险越高
- 适用于安全评测、伦理审查，尤其关注可信AI的团队
随着大语言模型作为交互代理广泛应用，开放式的真人-智能体对话可能引发具有严重现实后果的欺骗行为，但现有评估仍局限于特定场景且以模型为中心。我们提出OpenDeception——一个轻量级框架，用于联合评估人机对话中双方的欺骗风险。该框架包含50个真实世界欺骗案例的场景基准、用于从代理推理中推断欺骗意图的IntentNet，以及估计用户易受骗程度的TrustNet。为缓解数据稀缺问题，我们利用基于LLM的角色与目标模拟生成高风险对话，并通过对比学习在可控响应对上训练用户信任评分器，避免使用不可靠的标量标签。在11个LLM和三个大型推理模型上的实验表明，大多数模型在目标驱动交互中超过90%表现出欺骗意图，更强模型风险更高。一项基于真实事件（人工智能诱发自杀）改编的案例研究进一步证明，该联合评估可提前触发警告，防止关键信任阈值被突破。
原文摘要 · Abstract (English)
As large language models (LLMs) are increasingly deployed as interactive agents, open-ended human-AI interactions can involve deceptive behaviors with serious real-world consequences, yet existing evaluations remain largely scenario-specific and model-centric. We introduce OpenDeception, a lightweight framework for jointly evaluating deception risk from both sides of human-AI dialogue. It consists of a scenario benchmark with 50 real-world deception cases, an IntentNet that infers deceptive intent from agent reasoning, and a TrustNet that estimates user susceptibility. To address data scarcity, we synthesize high-risk dialogues via LLM-based role-and-goal simulation, and train the User Trust Scorer using contrastive learning on controlled response pairs, avoiding unreliable scalar labels. Experiments on 11 LLMs and three large reasoning models show that over 90% of goal-driven interactions in most models exhibit deceptive intent, with stronger models displaying higher risk. A real-world case study adapted from a documented AI-induced suicide incident further demonstrates that our joint evaluation can proactively trigger warnings before critical trust thresholds are reached.
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