Unveiling the Fragility of Vision-Language Models: Multi-Modal Adversarial Synergy via Texture-Constrained Perturbations and Cross-Modal Optimization
arXiv:2605.26501v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have transformed multi-modal understanding, excelling in tasks like image captioning and visual question answering by integrating visual and textual inputs. However, their robustness against adversarial attacks, particularly those exploiting both modalities, remains underexplored, posing risks to critical applications like autonomous driving and content moderation. Existing attacks focus on single modalities or require impractical white-box access, limiting their real-world relevance. In this paper, we intro
The rapid deployment and increasing sophistication of Large Vision-Language Models make understanding their vulnerabilities critical, especially as they move into high-stakes applications.
This research highlights fundamental robustness issues in multi-modal AI, impacting the reliability and safety of advanced AI systems destined for critical infrastructure like autonomous driving.
The understanding of AI security expands beyond single-modality attacks to encompass complex multi-modal vulnerabilities, necessitating more comprehensive adversarial training and evaluation.
- · AI security researchers
- · Adversarial AI startups
- · Developers of robust AI models
- · Companies deploying LVLMs in critical applications without robust security measu
- · Users relying on unhardened multi-modal AI systems
Increased awareness and research into multi-modal adversarial attacks on LVLMs.
Development of new defense mechanisms and industry standards for multi-modal AI robustness.
Delayed or more cautious adoption of LVLMs in highly sensitive sectors until robustness concerns are sufficiently addressed.
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Read at arXiv cs.AI