This work experimentally and systematically analyzes the differences between clean and jailbreak samples in the cross-attention feature space, revealing for the first time a cumulative separation effect and a progressively increasing trend of linear separability between the two during the diffusion process.
Si-Yuan Liang, Yupeng Qiu, Junfeng Fang et al.· 0 citations
The CVPR 2026@AdvML Workshop Challenge on adversarial multimodal attacks against autonomous-driving VLAs is presented, providing a practical reference for future robustness evaluation and defense design in multimodal autonomous-driving systems.
Tian-Yuan Zhang, Zonglei Jing, Jiangfan Liu et al.· arXiv.org· 0 citations
This work develops a four-part, intent-oriented taxonomy that organizes multi-turn jailbreaks by adversarial intent structure and finds that effectiveness is driven by how deliberately intent is organized across turns rather than by context length or query count.
Siyuan Li, Aodu Wulianghai, Zehao Liu et al.· 0 citations
This work studies the channel delivering search and page observations is a fragile security boundary and introduces Authority-Chain Hijack (ACH), an expert-refined strategy that turns isolated search-result and page-content manipulations into a coherent evidence chain across seemingly corroborating sources.
Xuebin Li, Han-Qing Zhao, Si-Yuan Liang et al.· 0 citations
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