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New JPPO attacks exploit vision-language models by optimizing pixels and prompts

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new adversarial framework called Joint Pixel-Prompt Optimization (JPPO) that targets vision-language models (VLMs). Unlike previous methods that focused on image perturbations, JPPO jointly optimizes both image pixels and user-visible prompts to amplify resource exhaustion attacks. This approach significantly increases latency and energy consumption in models like Qwen2.5-VL-7B and BLIP-2, revealing potential security vulnerabilities in current VLM serving defenses. AI

IMPACT Highlights potential security vulnerabilities in multimodal AI deployments, necessitating cost-aware robustness evaluations.

RANK_REASON The cluster contains an academic paper detailing a new research finding and methodology. [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 JPPO attacks exploit vision-language models by optimizing pixels and prompts

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The cluster contains an academic paper detailing a new research finding and methodology. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Zhaoxiong Ni, Yatie Xiao, Chi-Man Pun, Fei Peng, Qingxiao Guan, Keke Tang ·

    Multimodal Resource-Exhaustion Attacks on Vision-Language Models via Joint Pixel-Prompt Optimization

    arXiv:2609.05889v1 Announce Type: new Abstract: Resource-exhaustion attacks against autoregressive vision-language models (VLMs) typically assume unimodal threat models, treating the image branch as the primary optimization surface while holding user-visible prompts fixed. Even r…

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