Medium Risk

pictify_update_experiment

Update an existing experiment. Available fields depend on the current status: - draft/paused: All fields can be updated (name, slug, variants, goalConfig, banditConfig, hypothesis, etc.) - running: Only name, confidenceThreshold, minimumRunDays, goalConfig.destinationUrl - completed: Only name If...

Risk signalsHigh parameter count (18 properties)

Part of the Mcp server.

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

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AI agents use pictify_update_experiment to create or modify resources in Mcp. 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 pictify_update_experiment 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 Mcp.

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

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

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

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

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

What does the pictify_update_experiment tool do? +

Update an existing experiment. Available fields depend on the current status: - draft/paused: All fields can be updated (name, slug, variants, goalConfig, banditConfig, hypothesis, etc.) - running: Only name, confidenceThreshold, minimumRunDays, goalConfig.destinationUrl - completed: Only name If updating variants, weights must still sum to 10000.. It is categorised as a Write tool in the Mcp MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.

How do I enforce a policy on pictify_update_experiment? +

Register the MCP server in PolicyLayer and add a rule for pictify_update_experiment: 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 Mcp. Nothing to install.

What risk level is pictify_update_experiment? +

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

Can I rate-limit pictify_update_experiment? +

Yes. Add a rate_limit block to the pictify_update_experiment 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 pictify_update_experiment completely? +

Set action: deny in the PolicyLayer policy for pictify_update_experiment. 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 pictify_update_experiment? +

pictify_update_experiment is provided by the MCP server (@pictify/mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

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