---
title: "《当效率成为脆弱性：利用自适应无人机跟踪中的动态路由漏洞》"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2608.03902v1"
permalink: "https://feed.astralor.com/items/itm_66d2de390d3fa12cea2bee03"
published: "2026-08-04T16:29:35.000Z"
score: 78
channel: "ai"
tags: ["Adversarial Path-Inversion","arXiv","Lipschitz 分析","UAV 跟踪","动态路由","对抗攻击"]
---

# 《当效率成为脆弱性：利用自适应无人机跟踪中的动态路由漏洞》

## AI 点评

> 这篇论文给了我们一个重要的提醒：为了省算力而设计的动态路由，很可能是一扇未被锁上的后门。对那些正在把自适应 Transformer 部署到无人机、边缘设备上的工程师来说，现在知道攻击者可以靠肉眼不可见的扰动逼你走错路，比事后修复要划算得多。我特别欣赏他们把这个数学上的‘尖锐拐角’讲得如此清晰，下一步应该马上有人跟进防御端吧。

## 速览

该论文揭示了自适应 Transformer 跟踪器在追求效率的动态路由架构中存在数学结构缺陷：层跳过决策边界的 Lipschitz 奇异点会导致局部 Lipschitz 常数无界，使微小输入扰动即可被门控模块放大，引发推理拓扑剧变。研究者首次将此识别为可利用的新型攻击面，并提出对抗性路径反转（API）框架，通过生成难以察觉的扰动精确操纵门控决策，迫使模型进入改变后的计算路径，严重破坏其表征能力。实验表明，API 在先进自适应跟踪器上具备更高扰动隐蔽性、攻击有效性和更快推理速度，为动态跟踪网络安全性分析开辟了新维度，也为构建鲁棒自适应跟踪架构提供了理论警示。

## 关键点

- 自适应 Transformer 跟踪器的层跳过决策边界存在 Lipschitz 奇异点，微小输入扰动可被门控模块放大，导致推理拓扑剧变。
- 首次将这一数学不连续性识别为可利用的新型攻击面——拓扑路径空间攻击面。
- 提出对抗性路径反转（API）框架，用不可察觉扰动操控门控决策，使模型表征能力被破坏。
- 实验证明 API 在先进自适应跟踪器上具有更强的扰动隐蔽性、攻击效果和推理速度。

## 全文

Resource constraints on UAV platforms have driven a paradigm shift in aerial tracking, from pursuing performance toward balancing accuracy with efficiency. Adaptive Transformer Trackers, which leverage an input-dependent dynamic routing architecture, have emerged as a representative solution to this challenge. However, we reveal that behind this computation-on-demand flexibility hides a critical structural flaw: the Lipschitz singularity of computational path decisions, which has an unbounded local Lipschitz constant at discrete layer-skipping decision boundaries. This mathematical discontinuity renders adaptive tracking networks inherently unstable: tiny input perturbations can be amplified at the gating modules, causing dramatic changes in the inference topology. We formally characterize this singularity in the context of adaptive tracking architectures and, for the first time, identify it as a directly exploitable new attack surface. This insight reveals a previously overlooked and highly vulnerable topological path space attack surface. Based on this, we propose the Adversarial Path-Inversion (API) framework. API generates imperceptible perturbations to precisely manipulate the gating decisions, forcing the inference onto altered computational paths. The severe inconsistency between the original and the inverted paths dismantles the representation capability of the model. Extensive experiments on state-of-the-art adaptive trackers demonstrate that API achieves superior perturbation stealthiness, more effective attack, and faster inference speeds. This work opens a new dimension for the security analysis of dynamic tracking networks and provides a theoretical warning for constructing robust adaptive tracking architectures in the future.

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