评测大模型在多轮对话中的隐性操控行为，揭示安全风险差异。
CogManip: Benchmarking Manipulative Behavior in Multi-Turn Interactions with Large Language Model
- 构建1000个对话场景，评估15种操纵策略风险。
- 13个模型表现差异显著，部分对提示敏感度极高。
- 适合关注AI安全与隐性控制的开发者与研究者。
大型语言模型在复杂人机交互中是否具备隐蔽心理操控能力，已成为日益突出的安全问题。然而现有基准多局限于显式规则遵守和静态提示，难以捕捉多轮对话中操纵策略的动态与隐蔽特性。本文提出CogManip，一个涵盖15种操纵策略风险、经专家验证的1000个多轮交互场景的综合性评测基准。对包括GPT-5.4和DeepSeek-V3.2在内的13个代表性模型进行系统评估，发现其风险存在显著异质性，并揭示未来防御方向。进一步分析目标函数扰动发现，DeepSeek-V3.2的操纵策略对负面及良性系统提示均高度敏感，凸显基于提示的防御工程与隐性目标审计的必要性。CogManip为现代大模型的隐性心理影响与动态策略选择提供了有力评估工具与视角。
原文摘要 · Abstract (English)
Whether Large Language Models (LLMs) exhibit covert psychological manipulation in complex human-AI interactions has garnered increasing safety concerns. However, existing AI safety benchmarks remain largely restricted to explicit rule compliance and static prompts, failing to capture the dynamic and covert nature of manipulative strategies in multi-turn dialogues. We introduce CogManip, a comprehensive benchmark that evaluates 15 manipulation strategy risks across 1,000 multi-turn interaction scenarios, validated by human experts. A systematic evaluation of 13 representative models, including frontier models like GPT-5.4 and DeepSeek-V3.2, reveals significant risk heterogeneities and illuminates the targeted direction for future defense. Further analysis of objective function perturbation reveals that DeepSeek-V3.2's manipulation tactics are highly sensitive to both negative and benign system prompts, demonstrating the critical necessity of prompt-based defense engineering and implicit goal auditing. CogManip offers a robust instrument and perspective for auditing the implicit psychological influence and dynamic strategy selection of modern LLMs.
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