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 subset of transmitted vision tokens under a budget constraint. We propose four na\"ive attack strat
The increasing deployment of cloud-edge LLM inference architectures creates new attack surfaces for adversaries to exploit the data transmission layer. This research identifies a specific vulnerability that emerges directly from this architectural choice, becoming more relevant as distributed AI processing becomes standard.
This research highlights a critical security vulnerability in the emerging cloud-edge inference paradigm for large vision-language models, suggesting that current deployment strategies may be exposed to novel manipulation attacks. Strategic readers should care because it underscores the need for robust security protocols across AI system architectures, particularly where data transmission occurs between distributed components.
Existing cloud-edge LLM inference security models must now account for vision token manipulation as a viable attack vector, leading to a re-evaluation of data intergity and privacy protocols. This changes the understanding of 'secure' deployment in distributed AI systems.
- · AI cybersecurity firms
- · Cloud security providers
- · Model developers focusing on robust distributed architectures
- · Untransformed cloud-edge AI providers
- · Users of vulnerable LVLMs
- · General AI users trusting unsecure systems
Increased focus on securing intermediate data representations in distributed AI systems becomes a priority for developers and deployers.
New security standards and best practices for cloud-edge AI architectures will emerge, potentially increasing the cost and complexity of deployment.
The perceived trustworthiness of AI systems, particularly those operating in sensitive environments, could be eroded if these vulnerabilities are exploited in real-world scenarios.
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Read at arXiv cs.AI