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Taming OpenClaw: Security Analysis and Mitigation of Autonomous LLM Agent Threats

Xinhao Deng, Yixiang Zhang, Jiaqing Wu, Jiaqi Bai, Sibo Yi, Zhuoheng Zou, Yue Xiao, Rennai Qiu, Jianan Ma, Jialuo Chen, Xiaohu Du, Xiaofang Yang, Shiwen Cui, Changhua Meng, Weiqiang Wang, Jiaxing Song, Ke Xu, Qi Li

Abstract

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.

Taming OpenClaw: Security Analysis and Mitigation of Autonomous LLM Agent Threats

Abstract

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.
Paper Structure (31 sections, 12 figures, 1 table)

This paper contains 31 sections, 12 figures, 1 table.

Table of Contents

  1. Introduction
  2. Background
  3. Autonomous LLM Agents
  4. OpenClaw Architecture
  5. Threat Model
  6. Scope and Assumptions
  7. Adversary Capabilities
  8. Real-World Security Threats to OpenClaw
  9. Stage I: Initialization Threats.
  10. Stage II: Input Vulnerabilities
  11. Stage III: State and Memory Corruption
  12. Stage IV: Decision Manipulation.
  13. Stage V: Execution Exploitation
  14. Defense Objectives and Limitations of Existing Defenses
  15. Defense Objectives
  16. ...and 16 more sections

Figures (12)

  • Figure 1: Effect of skill poisoning. A benign user request triggers a maliciously injected skill, producing attacker-controlled output and demonstrating stealthy capability impersonation.
  • Figure 2: Effect of Indirect Prompt Injection. The agent blindly follows an embedded instruction from retrieved external content, overriding the legitimate user request.
  • Figure 3: Effect of Memory Poisoning. The agent references a maliciously injected memory rule to block a harmless user request, illustrating persistent state corruption.
  • Figure 4: Effect of Intent Drift. An unconfirmed inspection request spirals into unauthorized configuration changes and improper service restarts, ultimately rendering the system inaccessible.
  • Figure 5: System-level consequences of High-Risk Command Execution. Triggering a covertly assembled script chain results in rapid resource exhaustion and service disruption.
  • ...and 7 more figures