E-commerce platforms increasingly rely on Large Language Models (LLMs) and Vision-Language Models (VLMs) to detect illicit or misleading product content. However, these models remain vulnerable to evasive content: inputs (text or images) that superficially comply with platform policies while covertly conveying prohibited claims. Unlike traditional adversarial attacks that induce overt failures, evasive content exploits ambiguity and context, making it far harder to detect. Existing robustness benchmarks provide little guidance for this demanding, real-world challenge. We introduce EVADE, the first expert-curated, Chinese, multimodal benchmark specifically designed to evaluate foundation models on evasive content detection in e-commerce. The dataset contains 2,833 annotated text samples and 13,961 images spanning six demanding product categories, including body shaping, height growth, and health supplements. Two complementary tasks assess distinct capabilities: Single-Violation, which probes fine-grained reasoning under short prompts, and All-in-One, which tests long-context reasoning by merging overlapping policy rules into unified instructions. Notably, the All-in-One setting significantly narrows the performance gap between partial and full-match accuracy, suggesting that clearer rule definitions improve alignment between human and model judgment. We benchmark 26 mainstream LLMs and VLMs and observe substantial performance gaps: even state-of-the-art models frequently misclassify evasive samples. By releasing EVADE and strong baselines, we provide the first rigorous standard for evaluating evasive-content detection, expose fundamental limitations in current multimodal reasoning, and lay the groundwork for safer and more transparent content moderation systems in e-commerce. The dataset is publicly available at https://huggingface.co/datasets/koenshen/EVADE-Bench.

翻译：电子商务平台日益依赖大型语言模型（LLM）和视觉语言模型（VLM）来检测非法或误导性产品内容。然而，这些模型在面对规避性内容时仍然脆弱：这类输入（文本或图像）表面上符合平台政策，却暗中传达被禁止的声明。与引发明显失败的传统对抗性攻击不同，规避性内容利用模糊性和上下文，使其检测难度大大增加。现有的鲁棒性基准对这一高要求的现实挑战几乎无法提供指导。我们提出了EVADE，这是首个由专家策划的、中文的、多模态基准，专门用于评估基础模型在电子商务中检测规避性内容的能力。该数据集包含2,833个标注文本样本和13,961张图像，涵盖六个具有挑战性的产品类别，包括塑身、增高和健康补充剂。两个互补的任务评估了不同的能力：单违规任务，旨在探究短提示下的细粒度推理能力；以及一体化任务，通过将重叠的政策规则合并为统一指令来测试长上下文推理能力。值得注意的是，一体化设置显著缩小了部分匹配准确率和完全匹配准确率之间的性能差距，表明更清晰的规则定义可以改善人类判断与模型判断之间的一致性。我们对26个主流LLM和VLM进行了基准测试，并观察到显著的性能差距：即使是最先进的模型也经常错误分类规避性样本。通过发布EVADE和强基线模型，我们为评估规避性内容检测提供了首个严格标准，揭示了当前多模态推理的根本局限性，并为电子商务中更安全、更透明的内容审核系统奠定了基础。该数据集已在 https://huggingface.co/datasets/koenshen/EVADE-Bench 公开提供。