标题： 基于强化学习的文本到图像模型提示模板窃取 显示英文标题

标题： Reinforcement Learning-Based Prompt Template Stealing for Text-to-Image Models

摘要： 多模态大语言模型（MLLMs）已改变了文本到图像的工作流程，使设计师能够以前所未有的速度创建新颖的视觉概念。 这一进展催生了一个繁荣的提示交易市场，其中经过筛选的提示可以引发商标风格并被买卖。 尽管具有商业吸引力，提示交易也引入了一个 largely 未经审视的安全风险：提示本身可能被盗。 在本文中，我们揭示了这一漏洞，并提出了RLStealer，这是一种基于强化学习的提示反转框架，可以从少量示例图像中恢复其模板。 RLStealer将模板窃取视为一个顺序决策问题，并采用多种基于相似性的反馈信号作为奖励函数，以有效探索提示空间。 在公开可用的基准测试中进行的全面实验表明，RLStealer在减少总攻击成本至现有基线所需成本的13%以下的同时，达到了最先进的性能。 我们的进一步分析证实，RLStealer可以有效地跨不同图像风格进行泛化，以高效地窃取未见过的提示模板。 我们的研究突显了提示交易中固有的紧迫安全威胁，并为在新兴的MLLM市场中制定保护标准奠定了基础。 显示英文摘要

摘要： Multimodal Large Language Models (MLLMs) have transformed text-to-image workflows, allowing designers to create novel visual concepts with unprecedented speed. This progress has given rise to a thriving prompt trading market, where curated prompts that induce trademark styles are bought and sold. Although commercially attractive, prompt trading also introduces a largely unexamined security risk: the prompts themselves can be stolen. In this paper, we expose this vulnerability and present RLStealer, a reinforcement learning based prompt inversion framework that recovers its template from only a small set of example images. RLStealer treats template stealing as a sequential decision making problem and employs multiple similarity based feedback signals as reward functions to effectively explore the prompt space. Comprehensive experiments on publicly available benchmarks demonstrate that RLStealer gets state-of-the-art performance while reducing the total attack cost to under 13% of that required by existing baselines. Our further analysis confirms that RLStealer can effectively generalize across different image styles to efficiently steal unseen prompt templates. Our study highlights an urgent security threat inherent in prompt trading and lays the groundwork for developing protective standards in the emerging MLLMs marketplace.