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Security of OpenClaw Agents: Fundamentals, Attacks, and Countermeasures
Yuntao Wang, · 2026-05-26 · via cs.AI updates on arXiv.org

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Abstract:The rapid evolution of large language model (LLM)-driven autonomous agents has given rise to OpenClaw, a new class of open-source agent frameworks that operate as continuously running, skill-augmented systems with persistent memory, multi-channel interaction, and high degrees of autonomy. Such capabilities enable OpenClaw agents to autonomously execute complex, multi-step tasks and interact seamlessly with external applications, but simultaneously introduce a substantially enlarged attack surface. In particular, the combination of high-privilege operations and persistent memory exposes OpenClaw agents to various emerging threats, including skill poisoning, cognitive manipulation, multi-agent cascading failures, and supply-chain vulnerabilities. In this survey, we present a comprehensive study of the security landscape of OpenClaw agents. We first examine the general architecture and key characteristics that distinguish OpenClaw agents from traditional AI agent systems. We categorize existing security and privacy threats into a layered framework and analyze how vulnerabilities arise during agent reasoning, action execution, and external interaction. Representative defense mechanisms are also reviewed to draw the current defense landscape. Finally, several unresolved issues related to the reliability and trustworthiness of OpenClaw ecosystems are discussed.
Comments: 17 pages, 13 figures
Subjects: Artificial Intelligence (cs.AI)
Cite as: arXiv:2605.25435 [cs.AI]
  (or arXiv:2605.25435v1 [cs.AI] for this version)
  https://doi.org/10.48550/arXiv.2605.25435

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Yuntao Wang [view email]
[v1] Mon, 25 May 2026 05:25:39 UTC (2,185 KB)