Medium Risk

hivelearn_update_lesson_content

Replace just the content of a lesson — URL, content_type, and optional description/duration/thumbnail. Narrower than update_lesson; safe for bulk agent writes. Does NOT change title, sort_order, or module assignment.

Part of the HiveLearn server.

hivelearn_update_lesson_content can modify HiveLearn 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 hivelearn_update_lesson_content to create or modify resources in HiveLearn. 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 hivelearn_update_lesson_content 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 HiveLearn.

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

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

See the full HiveLearn policy for all 43 tools.

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These attack patterns abuse exactly the kind of access hivelearn_update_lesson_content 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 hivelearn_update_lesson_content 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 hivelearn_update_lesson_content tool do? +

Replace just the content of a lesson — URL, content_type, and optional description/duration/thumbnail. Narrower than update_lesson; safe for bulk agent writes. Does NOT change title, sort_order, or module assignment.. It is categorised as a Write tool in the HiveLearn MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.

How do I enforce a policy on hivelearn_update_lesson_content? +

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

What risk level is hivelearn_update_lesson_content? +

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

Can I rate-limit hivelearn_update_lesson_content? +

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

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

hivelearn_update_lesson_content is provided by the HiveLearn MCP server (hivelearn-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

Enforce policy on every HiveLearn tool call.

Deterministic rules across all 43 HiveLearn tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.

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