arXiv:2609.06991v1 Announce Type: cross
Abstract: Recent text-to-audio-video (T2AV) models jointly generate video, speech, sound effects, and ambience from a single text prompt. This capability poses...
By Suah Choi, Tae-Young Lee, Gyeong-Moon Park
Omni‑SafetyBench is a new benchmark designed to evaluate the safety of Omni‑Modal Large Language Models (OLLMs) that process visual, auditory, and textual data. It contains 23,328 test instances across 24 modality variations derived from 972 seed samples, and introduces metrics such as Safety‑score (based on Conditional Attack Success Rate and Conditional Refusal Rate) and Cross‑Modal Safety Consistency score. Evaluation of 11 state‑of‑the‑art OLLMs shows severe vulnerabilities, with only three models achieving a Safety‑score above 0.6 and safety degrading sharply for audio‑visual inputs, underscoring the need for improved safety alignment methods.
By Leyi Pan, Zheyu Fu, Yunpeng Zhai, Shuchang Tao, Sheng Guan, Shiyu Huang, Lingzhe Zhang, Zhaoyang Liu, Bolin Ding, Felix Henry, Aiwei Liu, Lijie Wen
arXiv:2603.02482v2 Announce Type: replace
Abstract: Safety evaluation of multimodal large language models requires tracking not only whether an attack succeeds, but also how the interaction unfolds a...
By Zhongxi Wang, Yueqian Lin, Jingyang Zhang, Zinuo Cheng, Hai Helen Li, Yiran Chen
arXiv:2606. 02111v1 Announce Type: cross Abstract: As multimodal large language models (MLLMs) have advanced to process video inputs, concerns have emerged about their potential for malicious misuse.
By Choongwon Kang, Seungjong Sun, Hyunmin Jun, Jang Hyun Kim
arXiv:2609.00206v1 Announce Type: cross
Abstract: Despite their growing use in video moderation, multimodal large language models (MLLMs) exhibit a compositional safety blind spot: videos composed of...
By Ruotong Wang, Zihao Zhu, Siwei Lyu, Xin Tao, Baoyuan Wu
arXiv:2606. 05177v1 Announce Type: cross Abstract: Existing multimodal safety benchmarks focus solely on visual inputs and cannot assess Omni Large Language Models (LLMs) that process vision, audio, and text.
By Manh Luong, Tamas Abraham, Junae Kim, Amar Kaur, Rollin Omari, Gholamreza Haffari, Trang Vu, Lizhen Qu, Dinh Phung