Despite the remarkable synthesis capabilities of text-to-image (T2I) models, safeguarding them against content violations remains a persistent challenge. Existing safety alignments primarily focus on explicit malicious concepts, often overlooking the subtle yet critical risks of compositional semantics. To address this oversight, we identify and formalize a novel vulnerability: Multi-Concept Compositional Unsafety (MCCU), where unsafe semantics stem from the implicit associations of individually benign concepts. Based on this formulation, we introduce TwoHamsters, a comprehensive benchmark comprising 17.5k prompts curated to probe MCCU vulnerabilities. Through a rigorous evaluation of 10 state-of-the-art models and 16 defense mechanisms, our analysis yields 8 pivotal insights. In particular, we demonstrate that current T2I models and defense mechanisms face severe MCCU risks: on TwoHamsters, FLUX achieves an MCCU generation success rate of 99.52%, while LLaVA-Guard only attains a recall of 41.06%, highlighting a critical limitation of the current paradigm for managing hazardous compositional generation.

翻译：尽管文本到图像模型展现出卓越的合成能力，但防范其内容违规仍是一个长期挑战。现有安全对齐主要聚焦于显式恶意概念，往往忽视组合语义中微妙但关键的风险。针对这一疏漏，我们识别并形式化了一种新型漏洞：多概念组合不安全性，其中不安全语义源于个体无害概念的隐含关联。基于该形式化定义，我们提出TwoHamsters基准测试，包含精心构建的17,500条提示词，旨在探测MCCU漏洞。通过对10个最先进模型和16种防御机制的严格评估，我们得出8项关键发现。特别地，我们证明当前文本到图像模型及防御机制面临严重的MCCU风险：在TwoHamsters上，FLUX模型的MCCU生成成功率达99.52%，而LLaVA-Guard的召回率仅为41.06%，凸显了当前范式在管理有害组合生成方面的关键局限。