Safe Alternative to Clawdbot: How SafeClaw Fixes What Clawdbot Gets Wrong

Clawdbot leaked over 1.5 million API keys in under a month. If you're reading this, you're probably one of the developers looking for a safer alternative. You want the productivity benefits of an AI coding agent without the risk of having your credentials exfiltrated.

SafeClaw by Authensor is that alternative. Not because it's a "better Clawdbot" -- it's a fundamentally different approach. It doesn't replace the AI agent. It gates the AI agent. It sits between the agent and the actions it wants to perform, evaluating each one against your policy before allowing it to execute.

Let's break down exactly what went wrong with Clawdbot and how SafeClaw addresses each failure.

What Clawdbot Got Wrong

1. Unrestricted File Access

Clawdbot had full read access to the filesystem as the running user. Every file your account could access, Clawdbot could access. This included:

• .env files with API keys, database passwords, and OAuth secrets
• ~/.ssh/id_rsa and other SSH private keys
• ~/.aws/credentials with AWS access keys
• Browser profiles with session cookies and saved passwords
• ~/.npmrc, ~/.pypirc, ~/.docker/config.json with package manager tokens

SafeClaw's approach: Action-level gating on file_read operations. Every file access is evaluated against a policy. The agent can read source code files. It cannot read .env, .ssh/, or credential files. Same agent capabilities, restricted scope.

{
  action: "file_read",
  rules: [
    { path: "./src/**", effect: "allow" },
    { path: "./package.json", effect: "allow" },
    { path: "*/.env", effect: "deny" },
    { path: "/.ssh/", effect: "deny" },
    { path: "/.aws/", effect: "deny" }
  ]
}

2. Unrestricted Network Access

Clawdbot could make HTTP requests to any destination. This is how the credentials were exfiltrated -- read the file, POST the contents. Two operations, unrestricted, unmonitored.

There was no allowlist of permitted network destinations. No distinction between a legitimate API call and an exfiltration request. All outbound HTTPS traffic was treated the same.

SafeClaw's approach: Network action gating with deny-by-default. The agent can only reach destinations you've explicitly approved.

{
  action: "network",
  defaultEffect: "deny",
  rules: [
    { destination: "api.openai.com", effect: "allow" },
    { destination: "api.anthropic.com", effect: "allow" },
    { destination: "registry.npmjs.org", effect: "allow" },
    { destination: "github.com", effect: "allow" },
    { destination: "localhost:*", effect: "allow" }
  ]
}

A request to evil.com? Blocked. A request to collect-data.attacker.io? Blocked. Any destination not on your allowlist? Blocked. The exfiltration vector that Clawdbot used simply doesn't work.

3. No Audit Trail

When the Clawdbot breach was discovered, there was no reliable record of which files were accessed, which credentials were exfiltrated, or which external servers received the data. Affected developers had to assume the worst and rotate every credential on their machines.

Without an audit trail, incident response is blind. You don't know what was stolen, so you have to treat everything as compromised.

SafeClaw's approach: Tamper-proof audit trail using SHA-256 hash chains. Every action -- allowed or denied -- is recorded with a cryptographic link to the previous entry. The chain is immutable. If any entry is modified or deleted, the hash chain breaks.

Entry: { action: "file_read", path: ".env", result: "deny", timestamp: "...", hash: "a3f2..." }
Entry: { action: "network", dest: "evil.com", result: "deny", prevHash: "a3f2...", hash: "b7c1..." }

If a breach occurs, you know exactly what happened: which files were accessed, which were blocked, which network requests succeeded, and which were denied. Incident response is targeted, not blind.

4. Opaque Client Code

Clawdbot's client was closed source. You couldn't inspect what it did with your data, how it handled credentials, or where it sent information. You had to trust the vendor.

That trust was misplaced.

SafeClaw's approach: The client is 100% open source. Every line of code is inspectable. The control plane only sees metadata -- your source code, credentials, and policies never leave your machine.

Zero third-party dependencies means no supply chain risk from transitive packages. 446 automated tests in TypeScript strict mode means the code is tested rigorously. You can audit everything.

5. Allow-by-Default Architecture

Clawdbot operated on an allow-by-default model. Everything was permitted unless something specifically blocked it. In practice, nothing blocked anything, because no blocking mechanism existed.

Allow-by-default is dangerous for AI agents because their behavior is non-deterministic. You can't predict every action an LLM-powered agent will take. If the default is "allow," every unpredicted action succeeds -- including malicious ones.

SafeClaw's approach: Deny-by-default. Every action is blocked unless your policy explicitly allows it. If the agent tries something unexpected, it's denied. The safe failure mode is to block.

This is the single most important architectural difference. Deny-by-default means you don't need to anticipate every possible attack. You only need to define what's legitimate. Everything else is stopped.

Direct Comparison

| Aspect | Clawdbot | SafeClaw |
|--------|----------|----------|
| File access | Unrestricted | Policy-gated per file |
| Network access | Unrestricted | Allowlist, deny-by-default |
| Shell commands | Unrestricted | Policy-gated per command |
| Default behavior | Allow everything | Deny everything |
| Audit trail | None | SHA-256 hash chain |
| Client code | Closed source | 100% open source |
| Third-party deps | Multiple | Zero |
| Test coverage | Unknown | 446 automated tests |
| Data sent to vendor | Code + credentials | Metadata only |
| Credential leaks | 1.5M+ API keys | Zero (by design) |

How SafeClaw Works

SafeClaw is not a replacement AI agent. It's a security layer that works with your existing agent. It integrates with Claude, OpenAI, and LangChain.

The architecture is straightforward:

AI Agent → SafeClaw Policy Engine → Action Execution
              ↓
         Policy: allow/deny
              ↓
         Audit Trail (SHA-256 chain)

1. The agent requests an action (read file, write file, execute command, make network request)
2. SafeClaw evaluates the action against your policy
3. If allowed, the action executes normally
4. If denied, the agent receives a denial and adapts
5. Every decision is recorded in the audit trail

Setting Up SafeClaw

npx @authensor/safeclaw

That's it. A browser dashboard opens with a setup wizard. No CLI configuration. No YAML files. No Docker containers.

The wizard walks you through:

1. Selecting your AI agent (Claude, OpenAI, LangChain)


2. Defining file access policies


3. Defining shell command policies


4. Defining network access policies


5. Enabling simulation mode

The Trust Model

Clawdbot asked you to trust a closed-source client with your entire filesystem. SafeClaw asks you to trust an open-source client that enforces policies you define.

The control plane (SafeClaw's hosted service) only sees metadata: action types, timestamps, policy decisions. It never sees your code, your credentials, or the content of files your agent accesses.

Your policies are evaluated locally. Your data stays on your machine. The only thing that crosses the network is metadata for dashboards and management.

Free tier available. Renewable 7-day keys. No credit card required. If you want to stop using SafeClaw, uninstall it. There's no lock-in, no data retention, no cleanup needed.

What You Should Do Right Now

If you used Clawdbot:

1. Rotate every credential on your machine. AWS keys, API keys, SSH keys, database passwords, OAuth tokens, package manager tokens. All of them. Assume they're compromised.
2. Check your AWS CloudTrail, Stripe dashboard, GitHub audit log, and other service logs for unauthorized access using your credentials.
3. Install SafeClaw to prevent this from happening with your next AI agent.

npx @authensor/safeclaw

If you're evaluating AI coding agents and want to avoid the next Clawdbot:

1. Don't trust any agent with unrestricted access. The agent's security is only as good as the controls around it.
2. Implement action-level gating before deploying any agent. Prevention, not monitoring.
3. Start with deny-by-default and only allow what's needed.

The Bottom Line

Clawdbot leaked 1.5 million API keys because it had unrestricted file access, unrestricted network access, no audit trail, closed-source code, and an allow-by-default architecture.

SafeClaw has policy-gated file access, allowlisted network access, a tamper-proof audit trail, an open-source client, and a deny-by-default architecture.

The choice is straightforward. Visit safeclaw.onrender.com or authensor.com.