The Shadow OpenClaw: How to Find Your Developers’ Autonomous Agents

The Rise of Autonomous Coding Agents

We are witnessing a shift from passive AI assistants to active, autonomous agents. While you are likely familiar with developers using ChatGPT or Copilot to write code, a new breed of tool has emerged: the autonomous agent.

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OpenClaw (formerly known as MoltBot and ClawdBot) is a prime example of this trend. It is an open-source AI agent framework that allows developers to run powerful LLM-driven agents locally. Unlike a browser tab, OpenClaw is designed to be fully integrated into the operating system. It can read and write files, execute terminal commands, and manage complex workflows without constant human supervision.

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For a developer, this is a productivity multiplier. For a CISO, it represents a significant blind spot in the identity and access management landscape.

Risks: Why OpenClaw Keeps Security Leaders Up at Night

The danger of OpenClaw lies not in its intent - which is usually benign productivity - but in its capabilities and permissions model. It fundamentally creates a "Shadow AI" problem that is far riskier than Shadow IT.

Implicit Trust and Excessive Permissions

OpenClaw runs with the full permissions of the user who installed it. If your senior engineer has write access to the production monorepo, so does their OpenClaw agent. If they have AWS credentials stored in ~/.aws/credentials, the agent can use them. There is no separation of duty; the agent is the user.

Credential Exposure

To function, these agents often require API keys for LLM providers (Anthropic, OpenAI) and other services (GitHub, Linear, Slack). These keys are typically stored in plain text configuration files (e.g., ~/.openclaw/config.json). This creates a centralized treasure trove of high-value credentials on developer endpoints, often without the protections of a managed secrets store.

Autonomous Execution

Unlike a chatbot that waits for a prompt, OpenClaw interacts with the system. It can modify code, execute scripts, and install dependencies. Capabilities like the Model Context Protocol (MCP) allow it to connect to databases and internal tools, effectively giving an AI model direct access to your infrastructure.

What We See in the Wild

In our research, we have observed OpenClaw deployments spreading across engineering organizations. The footprint is distinct but often ignored by standard security controls that aren't looking for AI-specific artifacts.

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Common indicators we see in the wild include:

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• Process Activity: Persistent Node.js processes or binaries named clawdbot, moltbot, or openclaw.
• File System Artifacts: Configuration directories in the user's home folder, specifically ~/.openclaw/, ~/.clawdbot/, or ~/clawd/.
• Workspace Data: "Memory" files (MEMORY.md) and session logs that contain sensitive project details and conversation history, often stored unencrypted on disk.
• MCP Servers: config.json files defining connections to local filesystems, PostgreSQL databases, and other "skills" that bridge the agent to the broader enterprise network.

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These artifacts suggest that the agent is not just installed but actively retaining context and credentials about your proprietary code and systems.

How to Detect OpenClaw

Detection is the first step toward governance. You don't need to guess if this is running in your environment - you can scan for it today.

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To help the community address this risk, we are promoting the OpenClaw Detector, an open-source tool designed to identify OpenClaw installations across your fleet.

The OpenClaw Detector

This GitHub repository (provided by Sun Security) provides lightweight, safe-to-execute scripts for macOS, Linux, and Windows. It scans for the known file paths, process names, and configuration patterns associated with OpenClaw and its variants.

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View the Detector on GitHub - can be run on ALL of your endpoints via EDR/MDM:
https://github.com/sun-security/openclaw-detector

Enterprise Deployment

For CISOs managing thousands of endpoints, the repository includes guidance on how to deploy these checks via MDM (like Jamf or Intune) or integrate the logic into your EDR queries (CrowdStrike, SentinelOne).

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The goal is not necessarily to block innovation, but to gain visibility. By detecting OpenClaw, you can bring these "shadow" agents into the light, ensuring they are configured securely and used responsibly within your organization's risk appetite.

Quick Detection

You can run a quick scan on a suspected endpoint using the provided one-liners:

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For macOS / Linux:

curl -sL https://raw.githubusercontent.com/sun-security/openclaw-detector/main/macos-linux/openclaw-detector.sh | bash

‍For Windows (PowerShell):

iwr -useb https://raw.githubusercontent.com/sun-security/openclaw-detector/main/windows/openclaw-detector.ps1 | iex