Building OpenClaw Security: Scanning AI Agent Configs and Skills Before They Bite
AI agents are moving from demos to production fast.
They can call tools, execute workflows, and interact with external systems — which is exactly why they introduce a new class of security risk.
I built OpenClaw Security to answer a simple question:
Before deploying an AI agent, can we quickly scan its configuration and skills for obvious security problems?
This post shares the motivation, what the scanner does today, and where I’d love feedback from engineers shipping real agent systems.
Why I started this
In several agent projects, I noticed the same pattern:
- teams iterate quickly on prompts, tools, and skills
- capabilities grow week by week
- security review happens late (or not at all)
Traditional AppSec tools are essential, but they often don’t understand agent-specific surfaces such as:
- tool permission scope
- skill-level side effects
- prompt-to-tool execution paths
- weak or missing guardrails in config
That gap inspired OpenClaw Security.
What OpenClaw Security scans today
OpenClaw Security currently focuses on two practical inputs:
- Agent config
- Skill definitions
The scanner looks for risky patterns and produces actionable findings.
1) Config scanning
Examples of checks:
- overly broad permissions
- unsafe defaults (e.g., missing constraints)
- unrestricted external tool access
- weak runtime policy settings
2) Skill scanning
Examples of checks:
- dangerous command execution patterns
- unvalidated input flowing into sensitive operations
- network/file/system operations with excessive privilege
- risky combinations of skill capability + missing guardrails
The goal is not “perfect formal verification.”
The goal is a fast, useful first security pass that helps teams catch high-risk issues early.
A simple risk model
I use a practical model while designing checks:
- Exposure: What can this agent/skill reach?
- Impact: If abused, what damage can happen?
- Control: What guardrails reduce misuse or prompt injection?
A finding is most concerning when all three are high:
high exposure + high impact + weak control.
This helps prioritize fixes instead of generating noisy “security theater.”
Example output format
A good scanner output should be easy to triage.
I aim for findings that include:
- severity
- location (config key / skill)
- why it matters
- concrete remediation suggestion
For example:
text
[HIGH] skill.deploy_shell
Reason: Executes shell commands with broad input surface.
Risk: Prompt injection may trigger arbitrary command execution.
Fix: Restrict command allowlist + require parameter validation + sandbox execution.
## Try it out
I am currently looking for early feedback from the community. If you are building or deploying AI agents, you can try the scanner for free here:
👉 **[OpenClaw Security Scanner](https://openclawsecurity.agency)**
I’d love to hear your thoughts: What other security checks would be most useful for your specific agent stack? Let me know in the comments!
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