标题： 纠缠威胁：量子机器学习安全的统一杀伤链模型 显示英文标题

标题： Entangled Threats: A Unified Kill Chain Model for Quantum Machine Learning Security

摘要： 量子机器学习（QML）系统继承了经典机器学习的漏洞，同时引入了源于量子计算的物理层和算法层的新攻击面。 尽管关于单个攻击向量的研究越来越多——从对抗性污染和逃避到电路级后门、侧信道泄露和模型提取——但这些威胁通常被孤立分析，且对攻击者的能力和系统环境做出了不现实的假设。 这种碎片化阻碍了有效、全面防御策略的发展。 在本工作中，我们认为QML安全需要更结构化的攻击面建模，不仅包括单独的技术，还包括它们之间的关系、前提条件以及在QML流程中的潜在影响。 我们提出将广泛用于经典IT和网络安全的杀伤链模型适应到量子机器学习的背景下。 此类模型允许对攻击者的目标、能力和可能的多阶段攻击路径进行结构化推理——涵盖侦察、初始访问、操纵、持久化和数据泄露。 基于广泛的文献分析，我们提出了一个详细的QML攻击向量分类法，该分类法映射到受经典机器学习MITRE ATLAS启发的量子感知杀伤链框架中的相应阶段。 我们强调了物理层威胁（如侧信道泄露和串扰故障）、数据和算法操纵（如污染或电路后门）以及隐私攻击（包括模型提取和训练数据推断）之间的相互依赖关系。 这项工作为新兴的量子机器学习领域中更现实的威胁建模和主动纵深安全设计提供了基础。 显示英文摘要

摘要： Quantum Machine Learning (QML) systems inherit vulnerabilities from classical machine learning while introducing new attack surfaces rooted in the physical and algorithmic layers of quantum computing. Despite a growing body of research on individual attack vectors - ranging from adversarial poisoning and evasion to circuit-level backdoors, side-channel leakage, and model extraction - these threats are often analyzed in isolation, with unrealistic assumptions about attacker capabilities and system environments. This fragmentation hampers the development of effective, holistic defense strategies. In this work, we argue that QML security requires more structured modeling of the attack surface, capturing not only individual techniques but also their relationships, prerequisites, and potential impact across the QML pipeline. We propose adapting kill chain models, widely used in classical IT and cybersecurity, to the quantum machine learning context. Such models allow for structured reasoning about attacker objectives, capabilities, and possible multi-stage attack paths - spanning reconnaissance, initial access, manipulation, persistence, and exfiltration. Based on extensive literature analysis, we present a detailed taxonomy of QML attack vectors mapped to corresponding stages in a quantum-aware kill chain framework that is inspired by the MITRE ATLAS for classical machine learning. We highlight interdependencies between physical-level threats (like side-channel leakage and crosstalk faults), data and algorithm manipulation (such as poisoning or circuit backdoors), and privacy attacks (including model extraction and training data inference). This work provides a foundation for more realistic threat modeling and proactive security-in-depth design in the emerging field of quantum machine learning.