---
title: "Who cites MM-SafetyBench: A Benchmark — 89 citing papers"
description: "89 papers of the Elamur Research library cite «MM-SafetyBench: A Benchmark for Safety Evaluation of Multimodal Large Language Models»…"
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# Who cites «MM-SafetyBench: A Benchmark for Safety Evaluation of Multimodal Large Language Models»

89 papers of our library cite it (of the 149,439 whose bibliographies we read) · 24 with a page here, the most cited first · [← back to «MM-SafetyBench: A Benchmark for Safety Evaluation of Multimodal Large Language Models»](https://md.research.elamur.ai/free/en/papers/liu-2023-mm-safetybench-benchmark-safety-evaluation/)

1. **[Improving Alignment and Robustness with Circuit Breakers](https://md.research.elamur.ai/free/en/papers/zou-2024-improving-alignment-robustness-circuit-breakers/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Andy Zou, Long Phan, Justin Wang, et al.*2024 · 83 citations*
2. [**JailBreakV: A Benchmark for Assessing the Robustness of MultiModal Large Language Models against Jailbreak Attacks arXiv:2404.03027**Weidi Luo, Siyuan Ma, Xiaogeng Liu, et al.*2024 · 66 citations*](https://md.research.elamur.ai/free/en/papers/luo-2024-jailbreakv-benchmark-assessing-robustness-multimodal/)
3. [**Safety Fine-Tuning at (Almost) No Cost: A Baseline for Vision Large Language Models arXiv:2402.02207**Yongshuo Zong, Ondrej Bohdal, Tingyang Yu, et al.*2024 · 60 citations*](https://md.research.elamur.ai/free/en/papers/zong-2024-safety-fine-tuning-almost-no/)
4. **[VLSBench: Unveiling Visual Leakage in Multimodal Safety](https://md.research.elamur.ai/free/en/papers/hu-2025-vlsbench-unveiling-visual-leakage-multimodal/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xuhao Hu, Dongrui Liu, Hao Li, et al.*2025 · 32 citations*
5. **[Jailbreak Large Vision-Language Models Through Multi-Modal Linkage](https://md.research.elamur.ai/free/en/papers/wang-2025-jailbreak-large-vision-language-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yu Wang, Xiaofei Zhou, Yichen Wang, et al.*2025 · 20 citations*
6. **[HiddenDetect: Detecting Jailbreak Attacks against Multimodal Large Language Models via Monitoring Hidden States](https://md.research.elamur.ai/free/en/papers/jiang-2025-hiddendetect-detecting-jailbreak-attacks-against/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yilei Jiang, Xinyan Gao, Tianshuo Peng, et al.*2025 · 15 citations*
7. **[Rethinking Jailbreak Detection of Large Vision Language Models with Representational Contrastive Scoring](https://md.research.elamur.ai/free/en/papers/hua-2026-rethinking-jailbreak-detection-large-vision/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Peichun Hua, Hao Li, Shanghao Shi, et al.*2026 · 10 citations*
8. **[VisCRA: A Visual Chain Reasoning Attack for Jailbreaking Multimodal Large Language Models](https://md.research.elamur.ai/free/en/papers/sima-2025-viscra-visual-chain-reasoning-attack/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Bingrui Sima, Linhua Cong, Wenxuan Wang, et al.*2025 · 9 citations*
9. **[Insight Over Sight: Exploring the Vision-Knowledge Conflicts in Multimodal LLMs](https://md.research.elamur.ai/free/en/papers/liu-2025-insight-over-sight-exploring-vision/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xiaoyuan Liu, Wenxuan Wang, Youliang Yuan, et al.*2025 · 6 citations*
10. **[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*
11. **[SafeMT: Multi-turn Safety for Multimodal Language Models](https://md.research.elamur.ai/free/en/papers/zhu-2026-safemt-multi-turn-safety-multimodal/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Han Zhu, Juntao Dai, Jiaming Ji, et al.*2026 · 3 citations*
12. **[MTMCS-Bench: Evaluating Contextual Safety of Multimodal Large Language Models in Multi-Turn Dialogues](https://md.research.elamur.ai/free/en/papers/liu-2026-mtmcs-bench-evaluating-contextual-safety/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zheyuan Liu, Dongwhi Kim, Yixin Wan, et al.*2026 · 1 citation*
13. **[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*
14. **[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*
15. **[AICA-Bench: Holistically Examining the Capabilities of VLMs in Affective Image Content Analysis](https://md.research.elamur.ai/free/en/papers/she-2026-aica-bench-holistically-examining-capabilities/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Dong She, Xianrong Yao, Liqun Chen, et al.*2026*
16. **[CrossGuard: Safeguarding MLLMs against Joint-Modal Implicit Malicious Attacks](https://md.research.elamur.ai/free/en/papers/zhang-2026-crossguard-safeguarding-mllms-against-joint/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xu Zhang, Hao Li, Zhichao Lu*2026*
17. **[DMN: A Compositional Framework for Jailbreaking Multimodal LLMs with Multi-Image Inputs](https://md.research.elamur.ai/free/en/papers/xu-2026-dmn-compositional-framework-jailbreaking-multimodal/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Wenzhuo Xu, Zhipeng Wei, Zonghao Ying, et al.*2026*
18. **[ECHA: Jailbreaking LVLMs via the Mismatch between Implicit Semantic Reconstruction and Explicit Safety Alignment](https://md.research.elamur.ai/free/en/papers/xu-2026-echa-jailbreaking-lvlms-mismatch-between/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Chenxing Xu, Junyong Jiang, Zehu Zhang, et al.*2026*
19. **[From Detection to Attribution: Forensic Linguistics and Adversarial Red Teaming as Complementary Responses to LLM Misuse](https://md.research.elamur.ai/free/en/papers/silva-2026-detection-attribution-forensic-linguistics-adversarial/) [International Conference on Natural Language Processing and Artificial Intelligence for Cyber Security](https://md.research.elamur.ai/free/en/venues/nlpaics/)**Rui Sousa-Silva*2026*
20. **[PII-VisBench: Evaluating Personally Identifiable Information Safety in Vision Language Models Along a Continuum of Visibility](https://md.research.elamur.ai/free/en/papers/shahariar-2026-pii-visbench-evaluating-personally-identifiable/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**G M Shahariar, Zabir Al Nazi, Md Olid Hasan Bhuiyan, et al.*2026*
21. **[Pruning Unsafe Tickets: A Resource-Efficient Framework for Safer and More Robust LLMs](https://md.research.elamur.ai/free/en/papers/si-2026-pruning-unsafe-tickets-resource-efficient/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Wai Man Si, Mingjie Li, Michael Backes, et al.*2026*
22. **[Reference Attack: A New Cross-Modal Jailbreaking Attack against Multimodal Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2026-reference-attack-new-cross-modal/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yulong Wang, Yifei Fu, Jiayi Gao*2026*
23. **[StructBreak: Structural Cognitive Overload-Induced Safety Failures in MLLMs](https://md.research.elamur.ai/free/en/papers/luo-2026-structbreak-structural-cognitive-overload-induced/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yang Luo, Liu Xinran, TianTian Ji, et al.*2026*
24. **[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*

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

- [← back to](https://md.research.elamur.ai/free/en/papers/liu-2023-mm-safetybench-benchmark-safety-evaluation/) — MM-SafetyBench: A Benchmark for Safety Evaluation of Multimodal Large Language Models