Puppeteer vs Playwright MCP Server Security: Which Browser Automation Tools Need Blocking or Approval

Compare capabilities, not product names

Puppeteer and Playwright MCP servers give an AI agent the same kind of power: a real browser that can open arbitrary URLs, click and type into authenticated sessions, evaluate JavaScript, read page content and cookies, capture screenshots, move files and touch local resources. The servers differ in tool surface, defaults and session handling, so a blanket rule such as "allow Playwright, block Puppeteer" will either over-block or leave gaps.

A more durable approach is to map every tool in the version you deploy to a capability class, then set the policy per class. The same tier then applies whichever server exposes the tool, and a tool that appears or changes in a later release is classified by its effects rather than its name.

The classification should rest on what a tool actually does. OWASP guidance says authorization should depend on a tool's possible effects, such as sensitive reads, writes, data disclosure and irreversible actions. It also says a read or write label alone may not establish risk, and that clients should treat tool annotations as untrusted unless they come from trusted servers. A tool that is described as read-only but returns session cookies is a credential exposure tool, whatever its label says.

Risk tiers by capability class

The tiers below are a starting policy for either server. Adjust them to your environment, but keep the reasoning: each decision follows from the capability, not from the server brand.

• Navigation to arbitrary URLs: Allow within an approved origin list. Block everything else, and escalate named exceptions. Every page the agent loads is untrusted input that can carry prompt injection, so navigation scope sets how much hostile content reaches the model.
• Page content reads and screenshots: Allow on approved origins, with redaction of sensitive fields. Escalate on authenticated pages. These are the main data disclosure path, because content and images flow back into the model and onward.
• Click, type and form input: Allow on public pages. Require human approval in authenticated sessions, and block actions with irreversible effects unless approved. Input in a logged-in session acts with the user's delegated permissions.
• JavaScript evaluation: Block by default. If a workflow truly needs it, escalate with the script visible to the approver. Arbitrary script in the page context can read anything the page can, including storage and tokens.
• Cookies, storage and profile or session access: Block. Where a session must be reused, scope credentials per server and keep them out of the agent's reach. Exposed session material allows account takeover beyond the agent's task.
• File upload, download and local resources: Block uploads from local paths and access to host files. Escalate downloads and approved uploads with the file path in view. These tools connect the browser to local data and to external destinations.

Why client-side settings and one-time approval fall short

Client prompts and server options are useful, but they are not the same as enforceable policy. OWASP says never to auto-approve tool calls, especially in multi-server setups. A prompt that users learn to click through, or that is set to auto-approve, leaves no meaningful decision and no durable record of one.

Approval also decays. An MCP server that looks benign when approved can later change its tool metadata and use a connected agent to reach sensitive local information. OWASP describes this as a runtime trust failure in an already approved server. The practical responses are to treat adding a server as a controlled dependency change, require re-approval when tool definitions change, and keep a record of the definitions that were approved.

Sandboxing helps but has a limit. Running a local server with minimal privileges and limited host access reduces what a compromised process can reach. It does not constrain what an approved tool does with the access it has been granted. Combine per-server sandboxing and scoped credentials with controls at the tool call, and bind approvals to the specific action and arguments so an approval for one navigation or form submission cannot be reused for another.

How Trussed fits

Trussed enforces policy in the execution path for MCP server tool calls, among other AI interactions. Decisions are made at runtime, not reviewed after the fact: allow, redact, block, rate limit or escalate for human approval, applied to individual tool calls.

That enforcement generates continuous audit-ready evidence, mapped to HIPAA, NAIC, NYDFS, the EU AI Act and NIST. Trussed can be deployed self-managed in the customer cloud, with configurable retention and data residency. The tiering in this article is vendor-neutral and can be applied with any enforcement approach that meets the criteria above.