Resource-Abuse Detection and Economic DoS Prevention

Determine how to distinguish legitimate high-volume use from adversarial resource abuse, detect prompts that induce excessive reasoning or tool calls, and control economic denial-of-service risks in cloud-based LLM services.

Background

Unbounded consumption attacks exploit the computational cost of inference by using long prompts, repeated requests, excessive reasoning, or chains of tool calls. Although rate limiting, resource caps, and anomaly detection provide practical defenses, the paper states that these controls do not reliably distinguish legitimate intensive use from abuse. Autonomous agents make the problem more difficult by chaining multiple model interactions, APIs, and external tools, potentially creating substantial operational costs and service degradation.