Resource Consumption Threats in Large Language Models
This is an incremental survey that addresses resource efficiency issues for LLM providers and users.
The paper surveys resource consumption threats in large language models (LLMs), which degrade efficiency by inducing excessive generation, harming service availability and economic sustainability, and aims to clarify the problem landscape for characterization and mitigation.
Given limited and costly computational infrastructure, resource efficiency is a key requirement for large language models (LLMs). Efficient LLMs increase service capacity for providers and reduce latency and API costs for users. Recent resource consumption threats induce excessive generation, degrading model efficiency and harming both service availability and economic sustainability. This survey presents a systematic review of threats to resource consumption in LLMs. We further establish a unified view of this emerging area by clarifying its scope and examining the problem along the full pipeline from threat induction to mechanism understanding and mitigation. Our goal is to clarify the problem landscape for this emerging area, thereby providing a clearer foundation for characterization and mitigation.