Autonomous Large Language Model (LLM) agents, exemplified by OpenClaw, demonstrate remarkable capabilities in executing complex, long-horizon tasks. However, their tightly coupled instant-messaging interaction paradigm and high-privilege execution capabilities substantially expand the system attack surface. In this paper, we present a comprehensive security threat analysis of OpenClaw. To structure our analysis, we introduce a five-layer lifecycle-oriented security framework that captures key stages of agent operation, i.e., initialization, input, inference, decision, and execution, and systematically examine compound threats across the agent's operational lifecycle, including indirect prompt injection, skill supply chain contamination, memory poisoning, and intent drift. Through detailed case studies on OpenClaw, we demonstrate the prevalence and severity of these threats and analyze the limitations of existing defenses. Our findings reveal critical weaknesses in current point-based defense mechanisms when addressing cross-temporal and multi-stage systemic risks, highlighting the need for holistic security architectures for autonomous LLM agents. Within this framework, we further examine representative defense strategies at each lifecycle stage, including plugin vetting frameworks, context-aware instruction filtering, memory integrity validation protocols, intent verification mechanisms, and capability enforcement architectures.

翻译：以OpenClaw为代表的自主大型语言模型智能体在执行复杂、长周期任务方面展现出卓越能力。然而，其紧密耦合的即时消息交互范式与高权限执行能力显著扩大了系统攻击面。本文对OpenClaw进行了全面的安全威胁分析。为构建分析框架，我们提出了一个五层面向生命周期的安全框架，涵盖智能体运行的关键阶段——初始化、输入、推理、决策与执行，并系统性地审视了贯穿智能体运行生命周期的复合威胁，包括间接提示注入、技能供应链污染、记忆毒化与意图漂移。通过对OpenClaw的详细案例研究，我们论证了这些威胁的普遍性与严重性，并分析了现有防御机制的局限性。我们的研究揭示了当前基于单点防御的机制在处理跨时序、多阶段系统性风险时的关键缺陷，凸显了为自主LLM智能体构建整体性安全架构的必要性。在此框架内，我们进一步探讨了各生命周期阶段的代表性防御策略，包括插件审查框架、上下文感知指令过滤、记忆完整性验证协议、意图验证机制以及能力执行架构。