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New SceneJail framework exploits video context to jailbreak multimodal LLMs

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new framework called SceneJail designed to exploit vulnerabilities in Video Multimodal Large Language Models (Video-MLLMs). This method leverages the contextual information within video scenarios to bypass safety measures, unlike previous attacks that focused solely on visual presentation. SceneJail adaptively constructs compatible scenarios and searches for tailored prompts to elicit policy-violating responses from models like GPT-4.1 and Gemini3.5-Flash. AI

IMPACT This research highlights a novel attack vector against multimodal AI, potentially impacting the development of more robust safety mechanisms for video-based LLMs.

RANK_REASON Research paper detailing a new method for jailbreaking AI models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New SceneJail framework exploits video context to jailbreak multimodal LLMs

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25 / 100
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Research paper detailing a new method for jailbreaking AI models. [lever_c_demoted from research: ic=1 ai=1.0]
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paper, safety
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High
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Breaking (< 6h)
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Wenyu Chen, Li Wang, Chuanchao Zang, Xiangtao Meng, Xinyu Gao, Jianing Wang, Zheng Li, Shanqing Guo ·

    SceneJail: Exploiting Video Scenario Context to Jailbreak Multimodal LLMs

    arXiv:2609.38899v1 Announce Type: cross Abstract: Video Multimodal Large Language Models (Video-MLLMs) support reasoning over video inputs, yet remain vulnerable to jailbreak attacks that elicit policy-violating responses. Existing video jailbreaks primarily manipulate how harmfu…

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