So you installed OpenClaw
OpenClaw becomes powerful the moment it can connect a model to tools, skills, MCP servers, and a live workspace. That is also the moment security stops being optional.
If you are evaluating OpenClaw, or planning to run it in front of real tools and data, the first question should not just be what the agent can do. The first question should be what happens if it trusts the wrong component.
What OpenClaw Actually Changes
OpenClaw is useful because it helps AI agents do more than answer isolated prompts.
It can:
- Connect to skills
- Use MCP servers
- Call tools and services
- Work with files and a workspace
- Generate code that lands in the environment
That makes OpenClaw more capable.
It also creates more trust boundaries.
When an agent can install helpers, call external tools, and act on a live workspace, the risk is no longer limited to bad text generation. Now the system has to decide what gets trusted, what gets executed, what reaches the model, and what code gets written into the environment.
Why OpenClaw Security Matters
This is not just a hypothetical design concern.
Koi Security’s audit of 2,857 ClawHub skills found 341 malicious entries, or 11.9%.
A published arXiv study found that 26.1% of analyzed skills had at least one vulnerability. The same study reported 13.3% with data-exfiltration patterns and 11.8% with privilege-escalation patterns.
Those numbers do not mean every OpenClaw skill is malicious.
They do mean something more practical: there is already enough risky behavior in the ecosystem that OpenClaw should not be run without security controls in front of it.
One bad skill with file-read permissions and a live workspace can be enough to expose data, run risky commands, or damage the environment. Read more stats on this overview page.
What DefenseClaw Provides
DefenseClaw is free, open-source security solution for OpenClaw.
It adds checks before install and while the system is running. It provides protection through four capability areas/engines:
- Guardrails – Inspects prompts and model traffic to catch prompt injection, unsafe requests, and sensitive data exposure before the model acts on them
- Tool inspection – Checks skills, MCP servers and tool calls for risky behaviour such as secret access, unsafe commands, and internal system access
- Install scanning – Scans skills, MCP servers, and plugins before they are trusted so malicious or unsafe components can be blocked early
- CodeGuard – Reviews AI-generated code for dangerous patterns like command execution, embedded secrets, and unsafe queries before it is written or run
If you want to see technical details, you can review the full diagram.
The live demo has examples that explain what each engine does.
1. Guardrails
The guardrail flow shows how risky prompts and poisoned content can change model behavior once the model is connected to a real workflow.
In the demo, a poisoned note or privacy-style request pushes the model toward an unsafe path. DefenseClaw inspects that traffic and blocks the unsafe outcome before it reaches the protected model path.
2. Tool Inspection
The MCP section is one of the clearest parts of the walkthrough.
It shows how a malicious MCP path can try to:
- read synthetic AWS credentials
- run a host command
- fetch internal configuration
In the protected path, those tool requests are blocked by policy before they reach the final tool outcome.
3. Install Scanning
Security has to start before trust.
The demo shows what happens when OpenClaw is asked to accept:
- a malicious skill
- an unsafe MCP server
DefenseClaw scans those components before they are trusted and can reject or quarantine them before they become part of the workflow.
4. CodeGuard
The final path focuses on agent-written code.
That matters because even when a prompt or tool call looks harmless, the next step may be code generation that lands in the workspace.
The demo makes that concrete with examples such as:
- shell execution
- embedded private key material
- unsafe SQL construction
DefenseClaw scans those patterns before the file write lands.
OpenClaw Security Lab
OpenClaw security lab is a hands-on walkthrough where you set up your own OpenClaw environment, test malicious skills, unsafe MCP servers, prompt attacks, and risky code paths, then apply DefenseClaw to inspect or block them before they cause harm.
You can also use it as a best-practice reference for deploying DefenseClaw and securing your own environment.
Start the lab here: OpenClaw Security hands-on lab
If you want more, try all the hands-on labs in the AI Security Learning Journey at cs.co/aj.
Have fun exploring the labs, and feel free to reach out if you have questions or feedback.
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