Medium Risk

preload_model

Preload a model into VRAM for warm inference. Sends an empty chat request with keep_alive to keep the model loaded during the session.

Part of the Claude Token Saver server.

preload_model can modify Claude Token Saver data, with no limits today. PolicyLayer puts allow, deny, and rate-limit rules on every call. Live in minutes.

SECURE CLAUDE TOKEN SAVER →

Free to start. No card required.

AI agents use preload_model to create or modify resources in Claude Token Saver. Write operations carry medium risk because an autonomous agent could trigger bulk unintended modifications. Rate limits prevent a single agent session from making hundreds of changes in rapid succession. Argument validation ensures the agent passes expected values.

Without a policy, an AI agent could call preload_model repeatedly, creating or modifying resources faster than any human could review. PolicyLayer's rate limiting ensures write operations happen at a controlled pace, and argument validation catches malformed or unexpected inputs before they reach Claude Token Saver.

Write tools can modify data. A rate limit prevents runaway bulk operations from AI agents.

policy.json
{
  "version": "1",
  "default": "deny",
  "tools": {
    "preload_model": {
      "limits": [
        {
          "counter": "preload_model_rate",
          "window": "minute",
          "max": 30,
          "scope": "grant"
        }
      ]
    }
  }
}

See the full Claude Token Saver policy for all 11 tools.

Get this rule live on your own Claude Token Saver server in minutes. PolicyLayer enforces it on every call, before it runs.

ENFORCE ON MY CLAUDE TOKEN SAVER →

View all 11 tools →

These attack patterns abuse exactly the kind of access preload_model gives an agent. Each links to the full case and the policy that stops it:

Browse the full MCP Attack Database →

Every attack above starts with a tool call. PolicyLayer checks each one against your policy first, so preload_model only ever does what you allow.

SECURE CLAUDE TOKEN SAVER →

Other write tools across the catalogue. The same approach applies to each: rate-limit and validate the arguments.

What does the preload_model tool do? +

Preload a model into VRAM for warm inference. Sends an empty chat request with keep_alive to keep the model loaded during the session.. It is categorised as a Write tool in the Claude Token Saver MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.

How do I enforce a policy on preload_model? +

Register the Claude Token Saver MCP server in PolicyLayer and add a rule for preload_model: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Claude Token Saver. Nothing to install.

What risk level is preload_model? +

preload_model is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.

Can I rate-limit preload_model? +

Yes. Add a rate_limit block to the preload_model rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.

How do I block preload_model completely? +

Set action: deny in the PolicyLayer policy for preload_model. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.

What MCP server provides preload_model? +

preload_model is provided by the Claude Token Saver MCP server (claude-token-saver-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

Enforce policy on every Claude Token Saver tool call.

Deterministic rules across all 11 Claude Token Saver tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.

Free to start. No card required.

4,600+ MCP servers and 31,000+ tools scanned and risk-classified.

// GET IN TOUCH

Have a question or want to learn more? Send us a message.

Message sent.

We'll get back to you soon.