mlsanityData to 14 Oct 2026

Understanding and mitigating the risks of OpenClaw for non-technical users: A practical guide with Skill

arXiv 2606.11007 ↗First posted 9 Jun 2026cs.CR cs.AI cs.SEcited by 0

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Abstract (arXiv)

OpenClaw has rapidly emerged as a transformative artificial intelligence (AI) agent framework, and its ability to autonomously execute complex, multi-step tasks has attracted an ever-growing and diverse user base. However, this capability comes with significant risks. While existing research has made important strides in characterizing these threats, such work is predominantly directed at technically sophisticated audiences. It remains largely inaccessible to non-technical users. This demographic now makes up an increasingly large and underserved portion of the community, yet it is these very users who most urgently need practical and straightforward guidance. In response, we bridge this gap through a series of interconnected efforts designed to lower the risk barrier for non-technical OpenClaw users. First, we identify and categorize seven core risks that OpenClaw users may encounter in daily usage, explaining each in plain language so that non-technical users can readily grasp the nature and potential consequences of these threats. Second, for each identified risk, we distill a set of corresponding defensive strategies into clear and actionable operational steps that are easy to follow. Third, to make protection even easier, we provide a companion OpenClaw Skill that automates key security configurations, enabling users to safeguard their systems with minimal manual intervention. Through this work, we demonstrate that safeguarding against the risks of intelligent agents need not be the exclusive domain of security experts, and that non-technical users can meaningfully participate in reducing these risks through simple, practical actions.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 15 of its 26 references that are in the map. First posted on arXiv 9 Jun 2026.

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15 of 26 references.

References · 15

  1. 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,234
  2. 2023Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injectioncited by 1,443
  3. 2022Taxonomy of Attacks on Open-Source Software Supply Chainscited by 58
  4. 2026Don't Let the Claw Grip Your Hand: A Security Analysis and Defense Framework for OpenClawcited by 29
  5. 2026Taming OpenClaw: Security Analysis and Mitigation of Autonomous LLM Agent Threatscited by 29
  6. 2026Formal Analysis and Supply Chain Security for Agentic AI Skillscited by 25
  7. 2026Uncovering Security Threats and Architecting Defenses in Autonomous Agents: A Case Study of OpenClawcited by 20
  8. 2026ClawKeeper: Comprehensive Safety Protection for OpenClaw Agents Through Skills, Plugins, and Watcherscited by 20
  9. 2026Your Agent, Their Asset: A Real-World Safety Analysis of OpenClawcited by 20
  10. 2026A Trajectory-Based Safety Audit of Clawdbot (OpenClaw)cited by 16
  11. 2026Clawdrain: Exploiting Tool-Calling Chains for Stealthy Token Exhaustion in OpenClaw Agentscited by 14
  12. 2026A Systematic Security Evaluation of OpenClaw and Its Variantscited by 13
3 more
  1. 2025Agent Tools Orchestration Leaks More: Dataset, Benchmark, and Mitigationcited by 11
  2. 2026ClawHub Security Signals: When VirusTotal, Static Analysis, and SkillSpector Disagreecited by 6
  3. 2026Benchmarking Security Risk Detection and Verification in Open Agentic Skill Ecosystemscited by 2

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