---
title: "Who cites VLSBench: Unveiling Visual — 28 citing papers"
description: "28 papers of the Elamur Research library cite «VLSBench: Unveiling Visual Leakage in Multimodal Safety», found in their bibliographies; the…"
canonical: "https://research.elamur.ai/free/en/papers/hu-2025-vlsbench-unveiling-visual-leakage-multimodal/cited-by/"
---
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[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [VLSBench: Unveiling Visual Leakage in Multimodal…](https://md.research.elamur.ai/free/en/papers/hu-2025-vlsbench-unveiling-visual-leakage-multimodal/) / cited by

# Who cites «VLSBench: Unveiling Visual Leakage in Multimodal Safety»

28 papers of our library cite it (of the 149,439 whose bibliographies we read) · 8 with a page here, the most cited first · [← back to «VLSBench: Unveiling Visual Leakage in Multimodal Safety»](https://md.research.elamur.ai/free/en/papers/hu-2025-vlsbench-unveiling-visual-leakage-multimodal/)

1. [**A Survey of Efficient Reasoning for Large Reasoning Models: Language, Multimodality, and Beyond arXiv:2503.21614**Xiaoye Qu, Yafu Li, Zhao-Chen Su, et al.*2025 · 55 citations*](https://md.research.elamur.ai/free/en/papers/qu-2025-survey-efficient-reasoning-large-reasoning/)
2. **[Layer-Aware Representation Filtering: Purifying Finetuning Data to Preserve LLM Safety Alignment](https://md.research.elamur.ai/free/en/papers/li-2025-layer-aware-representation-filtering-purifying/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Hao Li, Lijun Li, Zhenghao Lu, et al.*2025 · 11 citations*
3. **[Visual Contextual Attack: Jailbreaking MLLMs with Image-Driven Context Injection](https://md.research.elamur.ai/free/en/papers/ziqi-2025-visual-contextual-attack-jailbreaking-mllms/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Miao Ziqi, Yi Ding, Lijun Li, et al.*2025 · 6 citations*
4. **[LED-Merging: Mitigating Safety-Utility Conflicts in Model Merging with Location-Election-Disjoint](https://md.research.elamur.ai/free/en/papers/ma-2025-led-merging-mitigating-safety-utility/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Qianli Ma, Dongrui Liu, Qian Chen, et al.*2025 · 5 citations*
5. **[USB: A COMPREHENSIVE AND UNIFIED SAFETY EVALUATION BENCHMARK FOR MULTIMODAL LARGE LANGUAGE MODELS](https://md.research.elamur.ai/free/en/papers/zheng-2026-usb-comprehensive-unified-safety-evaluation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Baolin Zheng, Guanlin Chen, Qingyang Teng, et al.*2026 · 1 citation*
6. **[When Helpers Become Hazards: A Benchmark for Analyzing Multimodal LLM-Powered Safety in Daily Life](https://md.research.elamur.ai/free/en/papers/lou-2026-when-helpers-become-hazards-benchmark/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xinyue Lou, Xu Jinan, Jingyi Yin, et al.*2026 · 1 citation*
7. **[Multimodal Unlearning Across Vision, Language, Video, and Audio: Survey of Methods, Datasets, and Benchmarks](https://md.research.elamur.ai/free/en/papers/sarwar-2026-multimodal-unlearning-across-vision-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Nobin Sarwar, Shubhashis Roy Dipta, Zheyuan Liu, et al.*2026*
8. **[Visual Self-Fulfilling Alignment: Shaping Safety-Oriented Personas via Threat-Related Images](https://md.research.elamur.ai/free/en/papers/yang-2026-visual-self-fulfilling-alignment-shaping/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Qishun Yang, Shu Yang, Lijie Hu, et al.*2026*

20 more papers of our library cite it; they have no page here yet.

- [← back to](https://md.research.elamur.ai/free/en/papers/hu-2025-vlsbench-unveiling-visual-leakage-multimodal/) — VLSBench: Unveiling Visual Leakage in Multimodal Safety