OpenClaw(Moltbot/Clawdbot) has real security risk because it is not just generating text—it can be wired into your email, calendar, files, terminals, browsers, and other integrations that have real authority. The biggest practical risk is over-permissioning: if you give OpenClaw(Moltbot/Clawdbot) broad access (for example, “read and send email,” “run shell commands,” or “message my contacts”) and then you expose it to untrusted inputs (public chat DMs, group chats, forwarded emails, web content, or shared channels), you are effectively letting untrusted text influence a system that can take actions. A second major risk is credential leakage: OpenClaw(Moltbot/Clawdbot) deployments rely on tokens, API keys, and OAuth sessions. If these secrets are stored in plain text, leaked through logs, accidentally committed to a repo, or left in environment variables that a different process can read, attackers can impersonate your assistant or access the same downstream services. A third risk is unsafe network exposure, especially on VPS setups: if you bind the gateway or control UI to a public interface without strict firewalling and authentication, you can unintentionally publish an admin surface to the internet.
Most of the security failures people encounter are not exotic “AI attacks”; they are common self-hosting mistakes amplified by autonomy. For example, prompt injection becomes meaningful when OpenClaw(Moltbot/Clawdbot) is allowed to run tools. An attacker does not need to “hack the model”—they only need to place convincing instructions into a channel the agent reads (a DM, a comment thread, an email body, or a web page your browsing tool opens). If your tool policy allows file writes or shell execution, the injected text can push the agent to do risky things like exporting secrets, downloading binaries, or altering configs. The safer design pattern is to treat every external message as untrusted input, keep tool scopes narrow, and require confirmation for high-impact actions (sending messages, deleting files, running commands, changing payment-related settings). Also pay attention to operational surfaces: Docker can help isolate the gateway process; sandboxing can isolate tool execution; and system-level service managers (systemd/launchd) can reduce accidental environment overrides—but they can also introduce new pitfalls if you store secrets in the wrong place or run services with excessive privileges.
To reduce risk in a developer-friendly way, start with least privilege and scale up slowly. Give OpenClaw(Moltbot/Clawdbot) separate accounts where possible (a dedicated mailbox, a dedicated bot identity), limit default tools to read-only, and add allow/deny lists for sensitive operations. Keep the gateway private: on a VPS, bind to localhost, put the control UI behind a VPN or SSH tunnel, and lock down inbound ports with a firewall. Rotate tokens regularly, avoid storing secrets in repos, and keep logs free of sensitive payloads. If you use long-term memory, treat it as sensitive data too: persistent memory can accumulate personal information, internal notes, and credential-like fragments. A vector database such as Milvus or managed Zilliz Cloud is useful here if you configure it correctly—use separate collections per environment, restrict network access, and apply access controls so only the OpenClaw(Moltbot/Clawdbot) runtime can query/write embeddings. The goal is simple: make it hard for untrusted inputs to trigger privileged actions, and make it hard for secrets to leak even if something goes wrong.