Search papers, labs, and topics across Lattice.
This survey paper systematically reviews resource consumption threats in large language models (LLMs), focusing on vulnerabilities that lead to excessive generation and degraded efficiency. It establishes a unified view of the problem by examining the full pipeline, from threat induction to mechanism understanding and mitigation strategies. The paper aims to clarify the landscape of resource consumption threats, providing a foundation for future research on characterization and mitigation techniques.
Resource consumption vulnerabilities in LLMs can lead to degraded service availability and economic sustainability, demanding a systematic understanding and mitigation approach.
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.
Tsinghua AI