ChatbotManip: a Dataset to Facilitate Evaluation and Oversight of Manipulative Chatbot Behaviour

This paper introduces ChatbotManip, a novel dataset for studying manipulation in Chatbots.It contains simulated generated conversations between a chatbot and a (simulated) user, where the chatbot is explicitly asked to showcase manipulation tactics, persuade the user towards some goal, or simply be helpful.We consider a diverse set of chatbot manipulation contexts, from consumer and personal advice to citizen advice and controversial proposition argumentation.Each conversation is annotated by human annotators for both general manipulation and specific manipulation tactics.Our research reveals three key findings.First, Large Language Models (LLMs) can be manipulative when explicitly instructed, with annotators identifying manipulation in approximately 84% of such conversations.Second, even when only instructed to be "persuasive" without explicit manipulation prompts, LLMs frequently default to controversial manipulative strategies, particularly Gaslighting and Fear Enhancement.Third, zero-shot larger models such as Gemini 2.5 pro have the best performance in detecting manipulation (of the models tested), with more work required to fine-tune smaller open source models for real-world on-device oversight.Our work provides important insights for AI safety research and highlights the need of addressing manipulation risks as LLMs are increasingly deployed in consumer-facing applications.The dataset and code are publicly available at https://github.com/JContro /chatbotmanip_analysis.