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New black-box attack targets vision-language navigation systems

By PulseAugur Editorial · [1 sources] ·

Researchers have developed AdvNav, a novel black-box adversarial attack framework designed to disrupt Vision-and-Language Navigation (VLN) systems. Unlike previous methods requiring white-box access, AdvNav operates solely on observable inputs and outputs, making it more applicable to real-world deployed systems. The framework uses a dual-granularity behavior-based feedback mechanism to guide its optimization strategy, effectively discovering noise configurations that degrade navigation performance. Evaluations showed AdvNav achieved high attack success rates against transformer-based and LLM-based VLN models on the R2R dataset, highlighting critical perception vulnerabilities. AI

IMPACT This research highlights critical vulnerabilities in current vision-language navigation systems, potentially guiding the development of more robust and secure AI agents.

RANK_REASON The item is an academic paper detailing a new adversarial attack method for AI systems. [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 black-box attack targets vision-language navigation systems

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The item is an academic paper detailing a new adversarial attack method for AI systems. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Chenyang Li, Kaige Li, Zeyu Jiang, Changhao Chen ·

    AdvNav: Behavior-Guided Black-Box Adversarial Attacks on Vision-Language Navigation

    arXiv:2607.11063v1 Announce Type: new Abstract: Despite progress in Embodied AI, Vision-and-Language Navigation systems remain vulnerable to adversarial visual disturbances. Most existing methods rely on white-box access to target model gradients, which is often unrealistic for r…

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