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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.15967 |
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| _version_ | 1866911601699848192 |
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| author | Zhang, Chaoshuo Liang, Yibo Tian, Mengke Lin, Chenhao Zhao, Zhengyu Yang, Le Zhang, Chong Zhang, Yang Shen, Chao |
| author_facet | Zhang, Chaoshuo Liang, Yibo Tian, Mengke Lin, Chenhao Zhao, Zhengyu Yang, Le Zhang, Chong Zhang, Yang Shen, Chao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_15967 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | TwoHamsters: Benchmarking Multi-Concept Compositional Unsafety in Text-to-Image Models Zhang, Chaoshuo Liang, Yibo Tian, Mengke Lin, Chenhao Zhao, Zhengyu Yang, Le Zhang, Chong Zhang, Yang Shen, Chao Cryptography and Security Computer Vision and Pattern Recognition 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. |
| title | TwoHamsters: Benchmarking Multi-Concept Compositional Unsafety in Text-to-Image Models |
| topic | Cryptography and Security Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.15967 |