Large Language Models (LLMs) can generate highly persuasive text, raising concerns about their misuse for propaganda, manipulation, and other harmful purposes.This leads us to our central question: Is LLM-generated persuasion more difficult to automatically detect than human-written persuasion?To address this, we categorize controllable generation approaches for producing persuasive content with LLMs and introduce Persuaficial, a high-quality multilingual benchmark covering six languages: English, German, Polish, Italian, French and Russian.Using this benchmark, we conduct extensive empirical evaluations comparing human-authored and LLM-generated persuasive texts.We find that although overtly persuasive LLM-generated texts can be easier to detect than human-written ones, subtle LLMgenerated persuasion consistently degrades automatic detection performance.Beyond detection performance, we provide the first comprehensive linguistic analysis contrasting human and LLM-generated persuasive texts, offering insights that may guide the development of more interpretable and robust detection tools.