Medium Risk

vl_update_saved_object

Update a single Kibana saved object by type and ID. This performs a partial update - only the specified attributes will be changed, other attributes remain unchanged. Supports all saved object types (dashboard, visualization, index-pattern, search, config, lens, map, tag, canvas-workpad, canvas-e...

High parameter count (10 properties); Single-target operation

Part of the Kibana MCP server. Enforce policies on this tool with Intercept, the open-source MCP proxy.

AI agents use vl_update_saved_object to create or modify resources in Kibana. 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 vl_update_saved_object repeatedly, creating or modifying resources faster than any human could review. Intercept's rate limiting ensures write operations happen at a controlled pace, and argument validation catches malformed or unexpected inputs before they reach Kibana.

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

kibana.yaml
tools:
  vl_update_saved_object:
    rules:
      - action: allow
        rate_limit:
          max: 30
          window: 60

See the full Kibana policy for all 16 tools.

Tool Name vl_update_saved_object
Category Write
MCP Server Kibana MCP Server
Risk Level Medium

View all 16 tools →

Agents calling write-class tools like vl_update_saved_object have been implicated in these attack patterns. Read the full case and prevention policy for each:

Browse the full MCP Attack Database →

Other tools in the Write risk category across the catalogue. The same policy patterns (rate-limit, validate) apply to each.

What does the vl_update_saved_object tool do? +

Update a single Kibana saved object by type and ID. This performs a partial update - only the specified attributes will be changed, other attributes remain unchanged. Supports all saved object types (dashboard, visualization, index-pattern, search, config, lens, map, tag, canvas-workpad, canvas-element, etc.). IMPORTANT: Use version parameter for optimistic concurrency control to prevent conflicts.. It is categorised as a Write tool in the Kibana MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.

How do I enforce a policy on vl_update_saved_object? +

Add a rule in your Intercept YAML policy under the tools section for vl_update_saved_object. You can allow, deny, rate-limit, or validate arguments. Then run Intercept as a proxy in front of the Kibana MCP server.

What risk level is vl_update_saved_object? +

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

Can I rate-limit vl_update_saved_object? +

Yes. Add a rate_limit block to the vl_update_saved_object rule in your Intercept 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 vl_update_saved_object completely? +

Set action: deny in the Intercept policy for vl_update_saved_object. 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 vl_update_saved_object? +

vl_update_saved_object is provided by the Kibana MCP server (@tocharianou/mcp-server-kibana). Intercept sits as a proxy in front of this server to enforce policies before tool calls reach the server.

Enforce policies on Kibana

Open source. One binary. Zero dependencies.

npx -y @policylayer/intercept
github.com/policylayer/intercept →
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