Vision Token Manipulation Attacks on Cloud-Edge Inference of Large Vision-Language Models
📰 ArXiv cs.AI
Learn to defend against vision token manipulation attacks on cloud-edge inference of large vision-language models and understand their implications on AI security
Action Steps
- Identify potential attack surfaces in cloud-edge inference pipelines
- Implement encryption and authentication mechanisms to protect vision tokens
- Test and evaluate the robustness of vision-language models against vision token manipulation attacks
- Develop and deploy countermeasures to detect and mitigate VTM-Attacks
- Collaborate with cybersecurity experts to stay up-to-date with the latest threats and vulnerabilities
Who Needs to Know This
AI engineers, cybersecurity experts, and researchers working on large vision-language models and cloud-edge inference can benefit from this knowledge to improve the security of their models
Key Insight
💡 Vision token manipulation attacks can be launched in a black-box man-in-the-middle setting, highlighting the need for robust security measures
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🚨 Vision token manipulation attacks can compromise cloud-edge inference of large vision-language models! 🚨
Key Takeaways
Learn to defend against vision token manipulation attacks on cloud-edge inference of large vision-language models and understand their implications on AI security
Full Article
Title: Vision Token Manipulation Attacks on Cloud-Edge Inference of Large Vision-Language Models
Abstract:
arXiv:2607.02819v1 Announce Type: cross Abstract: Cloud-edge Large Vision-Language Model (LVLM) inference enables efficient deployment by splitting computation between edge devices and cloud servers. In this process, intermediate vision tokens are transmitted from the edge to the cloud over a communication link, thereby exposing a new attack surface. We study vision token manipulation attack (VTM-Attack) under a black-box man-in-the-middle setting, where an adversary intercepts and manipulates a
Abstract:
arXiv:2607.02819v1 Announce Type: cross Abstract: Cloud-edge Large Vision-Language Model (LVLM) inference enables efficient deployment by splitting computation between edge devices and cloud servers. In this process, intermediate vision tokens are transmitted from the edge to the cloud over a communication link, thereby exposing a new attack surface. We study vision token manipulation attack (VTM-Attack) under a black-box man-in-the-middle setting, where an adversary intercepts and manipulates a
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