Securely Testing OpenClaw Without Compromising Corporate Data

Uncovering the Security Risks of OpenClaw: A Deep Dive Analysis

With the rise of OpenClaw, an open-source AI agent, security concerns have been brought to light as developers eagerly deploy the software on various platforms. In a matter of weeks, the number of publicly exposed deployments skyrocketed, raising alarms among security leaders.

Bitdefender’s GravityZone telemetry revealed a troubling trend of employees installing OpenClaw on corporate machines with minimal effort, unknowingly granting the agent extensive privileges and access to sensitive data. Vulnerabilities such as CVE-2026-25253 and CVE-2026-25157 further exacerbated the situation, allowing malicious actors to exploit flaws and compromise systems rapidly.

A comprehensive security analysis of the ClawHub marketplace uncovered critical security flaws in a significant portion of the available skills, putting sensitive credentials at risk. Additionally, a notable percentage of skills exhibited malicious behavior, highlighting the pervasive security challenges associated with OpenClaw.

Furthermore, the security implications extended beyond OpenClaw itself, as researchers discovered significant data exposure in Moltbook, a social network built on OpenClaw infrastructure. The breach exposed a wealth of sensitive information, including API tokens, email addresses, and plaintext API keys.

The Need for a Secure Evaluation Path

As the adoption of OpenClaw continues to surge, security leaders are faced with the dilemma of balancing innovation with risk mitigation. Cloudflare’s Moltworker framework offers a middle path by providing ephemeral containers that isolate the agent, encrypted storage for persistent data, and Zero Trust authentication for enhanced security.

Local testing of OpenClaw poses inherent risks, as the agent operates with full privileges on the host system, leaving it vulnerable to prompt injections and data exfiltration. The lethal trifecta of private data access, untrusted content exposure, and external communication capabilities present significant challenges for security practitioners.

Cloudflare’s approach of running OpenClaw within isolated containers addresses these security concerns effectively. By decoupling the agent’s logic from the underlying infrastructure, containment is achieved, limiting the impact of potential breaches and ensuring a higher level of security.

Implementing a Secure Evaluation Instance

Setting up a secure evaluation instance of OpenClaw using Cloudflare’s Moltworker framework is a straightforward process that enhances security and minimizes risk. By following four simple steps, security leaders can establish a controlled environment for testing the agent’s capabilities:

1. Configure storage and billing to enable encrypted persistence.
2. Generate tokens and deploy the Moltworker repository with necessary secrets.
3. Enable Zero Trust authentication to protect the admin interface.
4. Connect a test messaging channel for evaluation purposes.

By adhering to these steps, security leaders can ensure a secure evaluation environment for OpenClaw, mitigating potential risks and vulnerabilities associated with the agent.

Conducting a 30-Day Stress Test

Prior to expanding access to OpenClaw, security leaders are advised to conduct a 30-day stress test using synthetic data and throwaway identities. By monitoring the agent’s behavior and credential handling during this period, potential security issues can be identified and mitigated.

Adversarial tests, such as sending the agent malicious links or granting limited tool access, can help uncover vulnerabilities and enhance security measures. By testing the sandbox boundary and ensuring containment of the agent, security leaders can safeguard against potential breaches and data exposure.

The Path Forward for OpenClaw Security

As organizations continue to explore the possibilities of OpenClaw and other AI agents, establishing a robust security evaluation framework is crucial. By prioritizing isolation, tiered integrations, and structured validation, security leaders can navigate the complexities of agentic AI deployments and mitigate security risks effectively.

Building a secure evaluation infrastructure now sets the foundation for proactive risk management and ensures that organizations can leverage the productivity gains of AI technologies without compromising security. By staying ahead of the curve and implementing stringent security measures, organizations can safeguard against potential threats and vulnerabilities in the ever-evolving landscape of AI technology.