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.

翻译：鉴于计算基础设施有限且成本高昂，资源效率是大型语言模型（LLM）的关键需求。高效的LLM能提升服务提供商的容量，同时降低用户的延迟和API成本。近期出现的资源消耗威胁会诱导模型过度生成，降低模型效率，并损害服务可用性与经济可持续性。本综述系统梳理了LLM中的资源消耗威胁问题，通过明确该新兴领域的研究范畴，并沿着从威胁诱发、机制理解到缓解治理的完整链路考察问题，建立了统一视角。我们的目标是阐明该新兴领域的问题全貌，从而为威胁表征与缓解提供更清晰的基础。