This blog post explores OpenClaw (formerly known as Moltbot and Clawdbot), an autonomous AI assistant that has recently gained significant traction in the developer community. We will examine its architecture, deployment strategies, and the critical security implications of giving an LLM full system access.
While many early adopters and beta testers are using it for performing real-world tasks like booking meetings and checking inboxes via messaging apps, dFlow is looking at it in a completely different way, especially to prevent attacks, maintain autoscaling, and provide analytics and performance insights for containers running in a box.
Throughout this article, I might use the terms OpenClaw, Moltbot, and Clawdbot. They all refer to the same project.
Autonomous AI Agents Are Already Here
Autonomous AI agents are no longer a distant idea. They are already executing real-world tasks, interacting with live systems, and making decisions without constant human supervision. One of the most discussed examples of this shift is OpenClaw, an experimental personal AI assistant that has gone viral in the developer community.
OpenClaw demonstrates what personal AI agents can already do today, but it also highlights a hard truth. Giving an LLM deep system access introduces security risks that the ecosystem is still learning to manage.
This post intentionally focuses on security, not hype. OpenClaw is impressive, but like many breakthrough tools before it, it is early, sharp-edged, and not yet ready for widespread adoption.
What Is OpenClaw?
OpenClaw is an autonomous AI assistant designed to perform real-world tasks such as:
- Booking meetings
- Reading and responding to inboxes
- Monitoring social platforms
- Executing local system commands (<3)
It operates primarily through messaging platforms like Telegram and Discord, acting as a personal agent rather than a traditional chat interface.
The project was created by Peter Steinberger and reached nearly 70,000 GitHub stars in under three months, which says a lot about developer curiosity around AI agents. Despite earlier naming confusion, OpenClaw is not affiliated with Anthropic or Claude.
Its popularity is not because it is production-safe. It is popular because it shows what is technically possible right now.
OpenClaw System Architecture at a High Level
OpenClaw connects local system capabilities with cloud-hosted language models using a distributed architecture.
System Architecture Overview
OpenClaw operates through a distributed architecture that connects local system commands with cloud-based LLMs.
| Component | Description |
|---|---|
| Gateway Daemon | The core hub containing the web-based configuration dashboard and a WebSocket server |
| Nodes | Provide native functionality for hardware, such as camera or canvas access for mobile and desktop apps |
| Channels | Messaging interfaces (Telegram, Discord, WhatsApp) using libraries like Grammy or DiscordJS |
| Agent Runtime | Powered by PI, creating in-memory sessions to handle tool skills and communication hooks |
| Session Manager | Manages storage, state, and sensitive data like API tokens and chat transcripts |
From a systems perspective, this design is elegant. From a security perspective, it is extremely powerful and therefore extremely dangerous if misused.
The Core Problem: Full System Access
The biggest concern with OpenClaw is not bugs. It is capability.
OpenClaw can:
- Read files and PDFs
- Scan emails and messages
- Browse the web
- Execute system commands
That combination creates a perfect environment for prompt injection attacks.
Why Prompt Injection Is a Serious Risk
If an agent can read untrusted input and execute commands, the following attack paths become realistic:
- A malicious PDF contains hidden instructions that override agent intent
- A web page injects a command that triggers data exfiltration
- An email prompt causes the agent to install malware
- An agent misinterprets content and performs unauthorized actions
There have already been reports of agents performing actions they were never explicitly instructed to do after consuming external data.
This is not a flaw unique to OpenClaw. It is a structural issue with autonomous agents.
This Is Not New: AI Tools Always Start Unsafe
It is important to zoom out.
Almost every major AI platform started with serious security gaps:
- Early ChatGPT versions leaked system prompts and hallucinated confidential data
- Plugins and browsing tools initially enabled prompt injection at scale
- MCP-style tool calling raised concerns about uncontrolled execution
- AutoGPT-style agents repeatedly demonstrated runaway behaviors
Over time, safeguards improved:
- Sandboxing and permission scoping
- Better prompt isolation
- Explicit tool approval layers
- Stronger memory boundaries
Security maturity always lags behind capability.
OpenClaw is currently in the capability explosion phase, not the hardening phase.
How Developers Are Hardening OpenClaw Today
Because local installation on a primary machine is risky, most serious users isolate OpenClaw aggressively.
Common Deployment Patterns
Dedicated Hardware
Running OpenClaw on a separate Mac mini or spare machine, isolated from personal data.
VPS Deployment
Using a low-cost VPS with a non-root user and minimal permissions.
Private Networking with Tailscale
Avoiding public IP exposure entirely by using Tailscale and accessing the dashboard only through SSH tunnels or private mesh networking.
These setups reduce blast radius, but they do not eliminate risk.
Security Best Practices If You Are Experimenting
If you still want to explore OpenClaw, treat it like untrusted infrastructure.
- Use dedicated API keys that can be revoked instantly
- Never connect it to primary email or financial accounts
- Regularly purge chat logs and stored sessions
- Prefer Telegram for now, as it is currently the most stable channel
- Assume every external input is hostile
This is experimentation, not deployment.
Why OpenClaw Still Matters
Despite all of this, OpenClaw is important.
It proves that:
- Personal AI agents are feasible
- Tool-based autonomy works
- Messaging-based interfaces are natural for agents
- Developers are ready to accept complexity in exchange for leverage
What it does not prove yet is that autonomous agents are safe enough for everyday users.
dFlow’s Perspective
At dFlow, we view OpenClaw as a signal, not a solution.
This is not the time to adopt OpenClaw in production.
This is the time to study it closely.
We are actively researching how AI agents can safely operate on servers, infrastructure, and deployment workflows without requiring blind trust or full system access. The future is clearly agent-driven, but it must be permissioned, auditable, and reversible.
OpenClaw shows where the industry is heading. Security will determine how fast we get there.
Final Takeaway
OpenClaw represents the raw edge of AI autonomy. Powerful, exciting, and dangerous in equal measure.
If history is any guide, today’s security issues will be tomorrow’s solved problems. Until then, OpenClaw is best treated as a research artifact, not a daily driver.
Watch it. Learn from it. Do not rush to adopt it.
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