Large Language Models (LLMs) have demonstrated impressive capabilities in text generation but raise concerns regarding potential copyright infringement. While prior research has explored mitigation strategies like content filtering and alignment, the impact of adversarial persuasion techniques in eliciting copyrighted content remains underexplored. This paper investigates how structured persuasion strategies, including logical appeals, emotional framing, and compliance techniques, can be used to manipulate LLM outputs and potentially increase copyright risks. We introduce a structured persuasion workflow, incorporating query mutation, intention-preserving filtering, and few-shot prompting, to systematically analyze the influence of persuasive prompts on LLM responses. Through experiments on state-of-the-art LLMs, including GPT-4o-mini and Claude-3-haiku, we quantify the effectiveness of different persuasion techniques and assess their implications for AI safety. Our results highlight the vulnerabilities of LLMs to adversarial persuasion and provide empirical evidence of the increased risk of generating copyrighted content under such influence. We conclude with recommendations for strengthening model safeguards and future directions for enhancing LLM robustness against manipulation. Code is available at https://github.com/Rongite/Persuasion.

Given a Large Language Model (LLM) generation, how can we identify which training data led to this generation? In this paper, we proposed RapidIn, a scalable framework adapting to LLMs for estimating the influence of each training data. The proposed framework consists of two stages: caching and retrieval. First, we compress the gradient vectors by over 200,000x, allowing them to be cached on disk or in GPU/CPU memory. Then, given a generation, RapidIn efficiently traverses the cached gradients to estimate the influence within minutes, achieving over a 6,326x speedup. Moreover, RapidIn supports multi-GPU parallelization to substantially accelerate caching and retrieval. Our empirical result confirms the efficiency and effectiveness of RapidIn.