Denial of Deadline: Network-Driven Accuracy Collapse in Distributed Inference Pipelines
How attackers can sabotage AI predictions by slowing down the system
Researchers discovered that distributed AI systems combining fast and slow prediction paths are vulnerable to a new type of attack: an attacker can flood the network with dummy requests to delay the slow, accurate path so badly that predictions arrive too late to be used. In a self-driving car test, 4,000 burst requests increased decision latency from 92 milliseconds to 2 seconds, cutting the accuracy benefit of the slower path in half and reducing object detection quality by 7 percentage points on average—with some rare objects like stop signs losing nearly half their detection accuracy.
Modern AI systems designed to be both fast and accurate rely on a backup slow path to improve predictions when time allows. This research reveals they can be attacked without stealing any model secrets or user data—only by congesting shared resources. Self-driving cars, drones, and other safety-critical systems using this architecture could fail to detect important objects in attacks, raising serious security questions for systems already deploying these hybrid inference designs.