English(EN) Jailbreak Connectivity: Towards Diverse, Transferable, and Universal Multimodal Large Language Model Jailbreak

新框架揭示多模态大语言模型安全方面的结构化漏洞

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-31 04:00

研究人员开发了一个名为越狱连接性（JC）的新框架，以解决多模态大语言模型（MLLMs）中的漏洞。该框架通过优化连续对抗路径来生成多样化且可迁移的视觉越狱攻击，从而绕过安全防护措施。JC 在 Qwen3-VL-8B-Instruct、InternVL3.5-8B 和 Gemma-3-12B-IT 等多种 MLLMs 上取得了显著的成功率，并在 GPT-5.6 和 Gemini 3.1 Pro 等模型的黑盒评估中表现优于现有方法。研究结果突显了 MLLMs 安全对齐方面的结构化弱点，并表明需要更强大的安全评估。 AI

影响强调了多模态人工智能潜在的安全风险，并提出了安全对齐的新评估方法。

排序理由详细介绍攻击多模态大语言模型新框架的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

Signal score: 0 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 详细介绍攻击多模态大语言模型新框架的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, safety; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: 3 days old; Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full source set.

完整方法见我们的编辑标准。

报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Xu Zhang, Ziqing Hu, Shuo Han, Ren Wang · 2026-10-31 04:00

越狱连接：迈向多样化、可迁移和通用多模态大语言模型越狱

arXiv:2610.38091v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) are vulnerable to visual jailbreak attacks that bypass safety safeguards. Existing methods typically optimize isolated adversarial examples, limiting attack diversity, transferability, and …

本文如何被排名

报道来源 [1]

越狱连接：迈向多样化、可迁移和通用多模态大语言模型越狱

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