AI agents are powerful but risky. Understand the security threats facing your OpenClaw deployment and how to protect against them.
Injection Attack
Prompt injection is when attackers craft inputs that manipulate your AI agent into ignoring its original instructions and executing malicious commands instead.
Access Control
Without proper authentication, anyone who discovers your OpenClaw endpoint can send commands, access data, and abuse your AI agent's capabilities.
Data Security
AI agents with network access can be tricked or compromised into sending sensitive data to attacker-controlled servers, bypassing traditional security controls.
Injection Attack
AI agents that can execute code are powerful but dangerous. Without proper sandboxing, attackers can run malicious code with full system privileges.
Access Control
Once attackers have initial access to your AI agent, they can exploit misconfigurations to gain higher privileges, eventually achieving full system control.
Infrastructure
Misconfigured reverse proxies and exposed dashboards give attackers direct access to your OpenClaw control panel, credentials, and connected services.
Access Control
WebSocket token leakage and CSRF-like vulnerabilities allow attackers to hijack your OpenClaw session by luring you to a malicious webpage.
When your secrets become public
When your AI agent goes off the rails
When you can't see what your AI did
When you can't stop your AI agent
When your dependencies are compromised
When your AI agent is exposed to the internet
When community plugins become attack vectors
When attackers exploit your trust in OpenClaw
Every threat on this page is something you need to solve yourself when self-hosting. Clawctl handles all of this for you.
You've seen the demo. OpenClaw running locally, doing incredible things. Writing code. Calling APIs. Actually getting stuff done.
So you think: "I should deploy this."
And that's when the fun stops.
Because now you're staring at an EC2 instance wondering:
You Google "secure openclaw deployment." You find nothing useful. You try setting up a reverse proxy. You realize auth bypass is trivial. You spend a weekend writing custom sandboxing. It breaks on the next update.
Sound familiar?
"I just want to ship the thing. Why is this so hard?"
Here's why: AI agents weren't built for production. They were built to be impressive. To demo well. To make you think "wow, the future is here."
But production isn't a demo. Production is where:
The gap between "works on my laptop" and "works in production" isn't a small gap.
It's a canyon. And most teams fall in.
Option 1: Don't deploy. Kill the project. Tell your team "it's not ready." Watch competitors ship while you wait.
Option 2: DIY hardening. Spend weeks building sandboxing, auth, logging, egress controls. Maintain it forever. Pray it doesn't break. Hope you didn't miss something obvious.
Option 3: Let someone who's already solved this handle it.
We've seen every threat on this page. We've blocked prompt injections, credential theft attempts, and rogue network calls. We've helped teams pass security reviews that seemed impossible.
Clawctl exists because we got tired of watching smart engineers waste weeks solving the same problems.
You run one command. Your agent deploys. It's already sandboxed, already logging, already blocking dangerous actions.
When the agent tries something sketchy? Blocked. You see exactly what happened. You can replay it. You can export it for your security team.
When your CTO asks "is this thing safe?" you don't hesitate. You show them the audit log. You show them the policy enforcement. You show them the kill switch.
You look like you know what you're doing. Because you do.
The math is simple:
Every day you run an unsecured agent in production is a day you're hoping nothing goes wrong.
Hope is not a security strategy.
Deploy your AI agent in 60 seconds with security that actually works. Cancel anytime.