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New VLM vulnerability: Scale-Induced Semantic Shift (SISS) discovered

By PulseAugur Editorial · [1 sources] ·

Researchers have identified a security vulnerability in vision-language models (VLMs) and web agents, termed Scale-Induced Semantic Shift (SISS). This occurs when image transformations, such as resizing or compression, alter the machine-readable meaning of an image. An attack framework combining dual-scale verification and other techniques achieved a 58.75% success rate on a benchmark of four closed-source VLMs. The effectiveness of SISS is bounded by aggressive compression, with success rates dropping significantly at higher resolutions. While SISS can increase hidden instruction perception in web agents, it leads to only about a 4% absolute behavioral compromise. AI

IMPACT Highlights a novel attack vector against VLMs and web agents related to image preprocessing, necessitating scale-consistent inference.

RANK_REASON Academic paper detailing a new security vulnerability in VLMs. [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 VLM vulnerability: Scale-Induced Semantic Shift (SISS) discovered

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Academic paper detailing a new security vulnerability in VLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Yiheng Zhao, Runze Ji, Chenyu Yi, Yi Yu, Bingquan Shen, Alex Kot, Xudong Jiang ·

    Scale-Induced Semantic Shift in Visual Prompt Injection Against VLMs and Web Agents

    arXiv:2611.16744v1 Announce Type: new Abstract: Visual content in multimedia systems may be resized, compressed, or re-rendered before it is interpreted by a vision-language model. We study a security failure mode in which these transformations change the machine-readable semanti…

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