The rapid adoption of OpenClaw has left security leaders struggling to find a safe way to test the autonomous agent without giving it shell access to their corporate laptops. In just under a week, the number of publicly exposed OpenClaw deployments grew from roughly 1,000 to over 21,000, with many employees installing it on their work machines using single-line install commands, thereby granting the agent shell access, file system privileges, and OAuth tokens to services like Slack, Gmail, and SharePoint.
The security risks associated with OpenClaw are significant, with vulnerabilities such as CVE-2026-25253 and CVE-2026-25157 allowing attackers to steal authentication tokens and execute arbitrary commands. A security analysis of the ClawHub marketplace found that 7.1% of the skills available contain critical security flaws, while a separate audit by Bitdefender found that 17% of the skills it analyzed exhibited malicious behavior. The Moltbook social network, built on OpenClaw infrastructure, also exposed 1.5 million API authentication tokens, 35,000 email addresses, and private messages containing plaintext OpenAI API keys due to a misconfiguration.
Security leaders need a controlled path to evaluate OpenClaw without putting their organization’s data at risk. Cloudflare‘s Moltworker framework provides a solution by using ephemeral containers that isolate the agent, encrypted R2 storage for persistent state, and Zero Trust authentication on the admin interface. This approach allows for secure testing and evaluation of OpenClaw without exposing the organization to unnecessary risks. Setting up a secure evaluation instance using Moltworker can be done in four steps: configuring storage and billing, generating tokens and deploying, enabling Zero Trust authentication, and connecting a test messaging channel.
The use of ephemeral containers changes the security dynamics of OpenClaw testing, as any compromised agent is trapped in a temporary container with zero access to the local network or files. The container dies when the task ends, eliminating the attack surface. This approach allows security leaders to test OpenClaw in a safe and controlled environment, without putting their organization’s data at risk. By following this approach, security leaders can develop a durable evaluation framework for agentic AI deployments, getting ahead of the shadow AI curve and capturing productivity gains while minimizing the risk of a breach.
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