Disrupting Vision-Language Model-Driven Navigation Services via Adversarial Object Fusion

TL;DR

This paper introduces AdvOF, an adversarial attack framework that manipulates 3D objects to disrupt vision-language navigation agents, revealing vulnerabilities in service-oriented perception systems and emphasizing the need for robustness.

Contribution

The paper presents a novel adversarial object fusion method targeting VLM-based navigation, with a detailed optimization process and extensive evaluation demonstrating its effectiveness.

Findings

AdvOF significantly degrades agent navigation performance.

The attack maintains minimal interference with normal tasks.

The work highlights security vulnerabilities in VLM-powered navigation systems.

Abstract

We present Adversarial Object Fusion (AdvOF), a novel attack framework targeting vision-and-language navigation (VLN) agents in service-oriented environments by generating adversarial 3D objects. While foundational models like Large Language Models (LLMs) and Vision Language Models (VLMs) have enhanced service-oriented navigation systems through improved perception and decision-making, their integration introduces vulnerabilities in mission-critical service workflows. Existing adversarial attacks fail to address service computing contexts, where reliability and quality-of-service (QoS) are paramount. We utilize AdvOF to investigate and explore the impact of adversarial environments on the VLM-based perception module of VLN agents. In particular, AdvOF first precisely aggregates and aligns the victim object positions in both 2D and 3D space, defining and rendering adversarial objects. Then, we collaboratively optimize the adversarial object with regularization between the adversarial and victim object across physical properties and VLM perceptions. Through assigning importance weights to varying views, the optimization is processed stably and multi-viewedly by iterative fusions from local updates and justifications. Our extensive evaluations demonstrate AdvOF can effectively degrade agent performance under adversarial conditions while maintaining minimal interference with normal navigation tasks. This work advances the understanding of service security in VLM-powered navigation systems, providing computational foundations for robust service composition in physical-world deployments.