一句话：首个针对RAG代码生成计算效率的对抗攻击，通过上下文投毒延长输出以推高GPU延迟与能耗。
现有研究多关注大语言模型（LLM）代码生成的质量与正确性，却较少审视检索增强生成（RAG）系统在安全视角下的计算成本。本文提出DrainCode，这是首个以RAG代码生成系统计算效率为目标的对抗攻击。该方法通过基于变异的上下文投毒策略，策略性地污染检索上下文，诱导LLM产生显著更长的输出，从而增加GPU延迟与能耗。作者在多个模型上评估了DrainCode，实验表明其相对基线最多可将延迟提高85%、能耗提高49%、输出长度增加3倍以上；攻击在不同提示策略下具有泛化性，并与多种防御方法进行了比较。该工作揭示了资源约束环境下LLM安全评估的一个新维度，并公开了代码与数据。
Large language models (LLMs) have demonstrated impressive capabilities in code generation, by leveraging retrieval-augmented generation (RAG) methods. However, the computational costs associated with LLM inference, particularly in terms of latency and energy consumption, have received limited attention in the security context. This paper introduces DrainCode, the first adversarial attack targeting the computational efficiency of RAG-based code generation systems. By strategically poisoning retrieval contexts through mutation-based approach, DrainCode forces LLMs to produce significantly longer outputs, thereby increasing GPU latency and energy consumption. We evaluate the effectiveness of DrainCode across multiple models. Our experiments show that DrainCode achieves up to a 85% increase in latency, a 49% increase in energy consumption, and more than a 3× increase in output length compared to the baseline. Furthermore, we demonstrate the generalizability of the attack across different prompting strategies and its effectiveness compared to different defenses. The results highlight DrainCode as a potential method for increasing the computational overhead of LLMs, making it useful for evaluating LLM security in resource-constrained environments. We provide code and data at https://github.com/DeepSoftwareAnalytics/DrainCode.
DOI 原文 ·
@inproceedings{paperbot9390,
title = {DrainCode: Stealthy Energy Consumption Attacks on Retrieval-Augmented Code Generation via Context Poisoning},
author = {Yanlin Wang and Jiadong Wu and Tianyue Jiang and Mingwei Liu and Jiachi Chen and Chong Wang and Ensheng Shi and Xilin Liu and Yuchi Ma and Zibin Zheng},
booktitle = {ASE},
year = {2025},
doi = {10.1109/ase63991.2025.00070}
}