Resource Consumption Threats in Large Language Models

arXiv cs.CL / 3/18/2026

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Key Points

It emphasizes resource efficiency as a key requirement for LLMs due to costly compute, aiming to improve service capacity while reducing latency and API costs.
It identifies threats that cause excessive resource consumption in LLMs, which can degrade efficiency, harm service availability, and threaten economic sustainability.
It provides a systematic review and a unified view by clarifying the scope and mapping the problem along the full pipeline from threat induction to mechanism understanding and mitigation.
It aims to establish a foundational framework for characterizing threats and guiding mitigation strategies in this emerging area.

Abstract

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.