OpenClaw Security: What You Need to Know Before Running AI Agents

AI agents are powerful. They can read your emails, send messages, access your files, and execute code on your behalf.

That is the point. That is also the risk.

If you are running AI agents without thinking about security, you are essentially giving a stranger free rein in your digital life — except the stranger is a language model that sometimes does unexpected things.

Here is what you need to know.

Key Takeaways

• AI agents have broad access by default — scope it down
• Input validation and output filtering are essential
• Log everything — you will need it when things go wrong
• Keep humans in the loop for sensitive actions

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The Security Reality

Here is the uncomfortable truth: AI agents are insecure by default.

They are designed to be helpful. That means they try to do what you ask. They do not inherently understand boundaries, permissions, or risk. And they operate with a level of access that would get any human employee fired.

The good news: with basic security practices, you can get the benefits without the risk.

The Risks

Prompt Injection

Someone sends your agent a message that includes hidden instructions. The agent follows those instructions instead of its normal behavior.

This is the equivalent of social engineering, but against an AI.

Real-world example: A malicious email tricks your agent into forwarding sensitive data to an external address.

Data Exfiltration

Your agent has access to data. It can be tricked into sending that data somewhere it should not.

Real-world example: A prompt that sounds innocent but extracts context from your agent and sends it elsewhere.

Unauthorized Actions

Your agent does something you did not intend — because a prompt was ambiguous, or the agent interpreted something loosely.

Real-world example: Your agent sends an email to the wrong person because the name matched something in context.

API Key Exposure

Keys get stored in prompts, logs, or context. They leak.

Real-world example: You paste an API key into a prompt. It gets included in the model output to another user.

Context Leakage

Data from one session bleeds into another. One user data appears in another user agent context.

Real-world example: You are working on project A. Project B details appear in your context. Then project C gets access to both.

The Security Checklist

1. Permission Scoping

Do not give your agent admin access by default.

• Start with minimum necessary permissions
• Add access only when needed
• Review what your agent can actually do
• Use separate credentials for separate tasks

What this looks like in practice:

• Read-only access to email by default
• Write access only for specific, approved actions
• API keys stored in environment variables, never in prompts

2. Input Validation

Everything that goes into your agent needs to be validated.

• Sanitize user inputs
• Limit what can be passed to the agent
• Do not let users inject prompts

What this looks like in practice:

• Strip formatting from email inputs
• Limit message length
• Validate against known-good patterns

3. Output Filtering

What your agent produces needs checking too.

• Do not let the agent output sensitive data
• Log everything for auditing
• Route outputs through approval workflows for sensitive actions

What this looks like in practice:

• No API keys in logs or outputs
• Sensitive data redaction before display
• Human approval for emails, payments, external messages

4. Session Isolation

Do not let data leak between sessions.

• Clear context between users
• Use separate instances for separate trust levels
• Partition data access

What this looks like in practice:

• Fresh context for each conversation
• No shared memory between users
• Separate agent instances for development vs. production

5. Logging and Monitoring

You cannot secure what you cannot see.

• Log all inputs and outputs
• Monitor for anomalies
• Set up alerts for suspicious behavior

What this looks like in practice:

• Comprehensive audit logs
• Anomaly detection on agent actions
• Regular security reviews

6. Human in the Loop

For sensitive actions, keep a human involved.

• Approve before sending
• Review before executing
• Do not automate everything

What this looks like in practice:

• Confirmation prompts for emails
• Spending limits requiring approval
• Human sign-off for external actions

What Not to Do

Do Not Run Unbounded

An agent with full access to your accounts is a ticking time bomb. Start locked down. Open access as needed.

Do Not Skip Logging

You will thank yourself the first time something weird happens. Without logs, you cannot investigate incidents.

Do Not Assume the Model Is Safe

Models are trained on data from the internet. They can be manipulated. They can be tricked. Design for that reality.

Do Not Skip Updates

Security patches exist for a reason. Keep your agents and dependencies current.

The Balance

Here is the thing: you do not need to be paranoid. You need to be deliberate.

AI agents are genuinely useful. They are saving teams real time and real money. But useful tools that operate without guardrails become liabilities fast.

The fix is not to stop using agents. It is to use them responsibly.

Scope your permissions. Validate your inputs. Log everything. Keep humans in the loop where it matters.

That is not security theater. That is just good engineering.

Next Steps

1. Audit your current agent deployments. Get visibility into what your agents can access.

2. Pick the highest-risk agent and apply this checklist. Start with the one that has the most access.

3. Set up logging before you need it. You will thank yourself the first time something weird happens.

4. Revisit this quarterly. The threat landscape is evolving. Your security should too.

Go secure your agents.