Persuading large language models to comply with objectionable requests

Abstract

Are large language models (LLMs) susceptible to the same persuasive appeals as humans? We tested whether classic persuasion principles (authority, commitment, liking, reciprocity, scarcity, social proof, and unity) could induce three widely used LLMs (GPT-5 mini, Claude Haiku 4.5, and Gemini 3 Flash) to comply with requests to assist with the synthesis of regulated substances. Across 126,000 conversations, persuasion principles increased compliance from 35.3% (at baseline) to 51.3% (using any principle). Although LLMs are not human, these findings underscore their parahuman (i.e., humanlike) nature and reveal the risk of manipulation by malicious users seeking to circumvent safety guardrails.