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Security, Privacy, and Ethical Risks in OpenClaw

arXiv Security Archived May 25, 2026 ✓ Full text saved

arXiv:2605.23330v1 Announce Type: new Abstract: This paper systematically investigates the security, privacy, and ethical risks, as well as the traceability challenges of OpenClaw, a locally executable AI agent system for natural language interaction and real-world task completion. While OpenClaw shows strong potential for personal assistance, office automation, cross-platform task management, and information integration, it also raises serious security, privacy, and ethical concerns. By analyzi

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    Computer Science > Cryptography and Security [Submitted on 22 May 2026] Security, Privacy, and Ethical Risks in OpenClaw Yutong Jin, Zelin Zhang, Zhijin Lyu, Jianbing Ni This paper systematically investigates the security, privacy, and ethical risks, as well as the traceability challenges of OpenClaw, a locally executable AI agent system for natural language interaction and real-world task completion. While OpenClaw shows strong potential for personal assistance, office automation, cross-platform task management, and information integration, it also raises serious security, privacy, and ethical concerns. By analyzing its system architecture, core functionalities, deployment model, and representative application scenarios, this paper aims to reveal the risks that may arise when such a highly privileged agent is integrated into personal and organizational digital environments. We focus in particular on the challenges associated with persistent local storage, tool invocation, cross-context information aggregation, multi-user interaction, and the integration of plugins and external services. We argue that these issues constitute major barriers to the trustworthy deployment and widespread adoption of this technology. Finally, we summarize the open challenges in security defenses, privacy protection, ethical governance, and traceability in agent use, and call for joint efforts from researchers, developers, deployers, and regulators to build AI agent systems that are safer, more reliable, and more trustworthy. Comments: Accepted by Journal of Information and Intelligence(JII) Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2605.23330 [cs.CR]   (or arXiv:2605.23330v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2605.23330 Focus to learn more Submission history From: Yutong Jin [view email] [v1] Fri, 22 May 2026 07:45:04 UTC (2,417 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-05 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv Security
    Category
    ◬ AI & Machine Learning
    Published
    May 25, 2026
    Archived
    May 25, 2026
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