Denial of Deadline: Network-Driven Accuracy Collapse in Distributed Inference Pipelines

2026-07-27

AI Summary

This work reveals a novel attack surface in edge-cloud collaborative dual-path distributed inference systems: malicious “oscillating burst” traffic can induce resource contention in the slow path, causing benign requests to time out and be dropped, thereby triggering an “accuracy collapse” wherein the system degrades to low-precision fast-path outputs. Notably, this attack requires no access to the model or data and operates solely through network-level interference, significantly degrading perceptual performance. Evaluated on a multi-object tracking simulation platform under autonomous driving scenarios, approximately 4,000 burst requests increased the p99 latency for benign users from 92 ms to 2 seconds, reduced average HOTA by 7.0 points, and caused nearly 50% accuracy loss on rare classes such as stop signs.

Abstract

Inference systems increasingly combine a fast path that returns predictions within the application's latency deadline together with a higher-accuracy slow path that runs higher-compute methods on stronger, remote hardware, so its results can be returned on time and combined with the fast path predictions. Across several application domains, we abstract this inference architecture as a fast path, a slow path, and a coordination layer with two functions: a router that invokes the slow path and a merger that decides whether to incorporate its returned predictions. In this work, we show that this new coordination layer exposes a new attack surface: shaped workload attacks, e.g., Yo-Yo bursts, can exploit contention at shared resources along the slow path to push benign users' slow-path predictions past their latency deadlines. The merger then discards those predictions, while the fast path continues to return timely outputs. We refer to the resulting loss of slow-path accuracy benefits as accuracy collapse. We demonstrate accuracy collapse in a two-tier edge-cloud multi-object tracking pipeline in autonomous driving. In simulation, approximately 4,000 burst-shaped requests increase benign p99 latency from 92ms to 2s, nearly eliminating the benefit of the slow path's cloud inference, reducing object tracking quality by 7.0 HOTA points on average. We further find that accuracy degradation can significantly vary (2.0-18.7 HOTA points), depending on the video intervals that are targeted in the attack, and that certain rare classes (e.g., stop signs) lose nearly half of their pre-attack prediction accuracy. These results show that workload attacks can degrade prediction quality without needing either access to model weights or victim data, and motivate research on attacks and defenses for routing, merging, scheduling, and resource isolation in these emerging inference pipeline architectures.

Problem

• accuracy collapse
• distributed inference pipelines
• workload attacks
• latency deadline
• slow path

Innovation

• accuracy collapse
• shaped workload attacks
• distributed inference pipelines
• latency deadline violation
• edge-cloud inference