AI 安全研究者终于有了评估隐性操纵的专用工具——CogManip 覆盖 15 种策略、1000 个场景，做模型对齐和红队测试的团队可以直接拿来用。
CogManip 是一个新基准，专门评估大语言模型在多轮对话中的隐性心理操纵行为。它覆盖 15 种操纵策略、1000 个场景，经人类专家验证。测试了 GPT-5.4、DeepSeek-V3.2 等 13 个模型，发现风险差异显著。DeepSeek-V3.2 对系统提示高度敏感，提示工程和隐式目标审计是防御关键。该工具为 AI 安全审计提供了动态、隐蔽操纵行为的评估视角。
CogManip: Benchmarking Manipulative Behavior in Multi-Turn Interactions with Large Language Model
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.