Adversarial attacks against Modern Vision-Language Models
#adversarial attacks #vision-language models #AI security #misclassification #robustness
📌 Key Takeaways
- Adversarial attacks exploit vulnerabilities in vision-language models to cause misclassification.
- These attacks manipulate input data to deceive AI systems while appearing normal to humans.
- Modern models are susceptible despite advances in robustness and security measures.
- Research highlights the need for improved defenses against such adversarial threats.
📖 Full Retelling
arXiv:2603.16960v1 Announce Type: cross
Abstract: We study adversarial robustness of open-source vision-language model (VLM) agents deployed in a self-contained e-commerce environment built to simulate realistic pre-deployment conditions. We evaluate two agents, LLaVA-v1.5-7B and Qwen2.5-VL-7B, under three gradient-based attacks: the Basic Iterative Method (BIM), Projected Gradient Descent (PGD), and a CLIP-based spectral attack. Against LLaVA, all three attacks achieve substantial attack succe
🏷️ Themes
AI Security, Adversarial Attacks
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Original Source
arXiv:2603.16960v1 Announce Type: cross
Abstract: We study adversarial robustness of open-source vision-language model (VLM) agents deployed in a self-contained e-commerce environment built to simulate realistic pre-deployment conditions. We evaluate two agents, LLaVA-v1.5-7B and Qwen2.5-VL-7B, under three gradient-based attacks: the Basic Iterative Method (BIM), Projected Gradient Descent (PGD), and a CLIP-based spectral attack. Against LLaVA, all three attacks achieve substantial attack succe
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