MLLM Safety

MLLM: Multimodal Large Language Model

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Latest papers

36 papers9 papers in the last four weeks, against 2 the four weeks before. 0.1% of all new papers.

Structured Visual Narratives Undermine Safety Alignment in Multimodal Large Language Models
Mar 23, 2026Rui Yang Tan, Yujia Hu, Roy Ka-Wei LeeMLLM Safety Adversarial Attacks on VLMs
Singapore University of Technology and Design. · University of British Colombia.
OOD-MMSafe: Advancing MLLM Safety from Harmful Intent to Hidden Consequences
Mar 10, 2026Ming Wen, Kun Yang, Jingyu Zhang +4MLLM Safety LLM Safety
Fudan University, Shanghai, China · Shanghai Innovation Institute, Shanghai, China · Ant Group, Hangzhou, China +1
SaFeR-ToolKit: Structured Reasoning via Virtual Tool Calling for Multimodal Safety
Mar 3, 2026Zixuan Xu, Tiancheng He, Huahui Yi +7Cultural Safety Evaluation of VLMs VLM Safety
Huazhong University of Science and Technology · Beijing University of Posts and Telecommunications · West China Hospital, Sichuan University +1
MUSE: A Run-Centric Platform for Multimodal Unified Safety Evaluation of Large Language Models
Mar 3, 2026Zhongxi Wang, Yueqian Lin, Jingyang Zhang +3LLM Safety Benchmarks Jailbreak Attacks
1Duke University · 2Virtue AI
Towards Multi-modal Multi-turn Safety: From Agentic Interaction to Strategic Alignment
Jan 8, 2026Han Zhu, Jiale Chen, Chengkun Cai +8Language Model Safety Evaluation Multimodal Large Language Models
Hong Kong University of Science and Technology · University College London · AISpeech +1
SGM: Safety Glasses for Multimodal Large Language Models via Neuron-Level Detoxification
Dec 17, 2025Hongbo Wang, AprilPyone MaungMaung, Isao EchizenMLLM Safety Multimodal Robustness
1Graduate School of Information Science and Technology, The University of Tokyo, Japan · 2Information and Society Research Division, National Institute of Informatics, Japan · 3The Graduate University for Advanced Studies, SOKENDAI, Japan

Mar 23, 2026J/K move · Enter open · S save

Structured Visual Narratives Undermine Safety Alignment in Multimodal Large Language Models

Rui Yang Tan, Yujia Hu, Roy Ka-Wei Lee

Singapore University of Technology and Design. · University of British Colombia.

MLLM Safety Adversarial Attacks on VLMs Adversarial Attacks on LLMs LLM Safety Alignment LLM Safety Benchmarks Language Model Safety Evaluation

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Multimodal Large Language Models (MLLMs) extend text-only LLMs with visual reasoning, but also introduce new safety failure modes under visually grounded instructions. We study comic-template jailbreaks that embed harmful goals inside simple three-panel visual narratives and prompt the model to role-play and "complete the comic." Building on JailbreakBench and JailbreakV, we introduce ComicJailbreak, a comic-based jailbreak benchmark with 1,167 attack instances spanning 10 harm categories and 5 task setups. Across 15 state-of-the-art MLLMs (six commercial and nine open-source), comic-based attacks achieve success rates comparable to strong rule-based jailbreaks and substantially outperform plain-text and random-image baselines, with ensemble success rates exceeding 90% on several commercial models. Then, with the existing defense methodologies, we show that these methods are effective against the harmful comics, they will induce a high refusal rate when prompted with benign prompts. Finally, using automatic judging and targeted human evaluation, we show that current safety evaluators can be unreliable on sensitive but non-harmful content. Our findings highlight the need for safety alignment robust to narrative-driven multimodal jailbreaks.

Latest papers

Structured Visual Narratives Undermine Safety Alignment in Multimodal Large Language Models

OOD-MMSafe: Advancing MLLM Safety from Harmful Intent to Hidden Consequences

SaFeR-ToolKit: Structured Reasoning via Virtual Tool Calling for Multimodal Safety

MUSE: A Run-Centric Platform for Multimodal Unified Safety Evaluation of Large Language Models

Towards Multi-modal Multi-turn Safety: From Agentic Interaction to Strategic Alignment

SGM: Safety Glasses for Multimodal Large Language Models via Neuron-Level Detoxification