Medium Risk

generate_video

Start an async AI video generation job \u2014 returns a job_id immediately. Poll with check_status every 10-30s until complete. Cost varies by model: veo3-fast (~15 credits/5s), kling-3 (~30 credits/5s), sora2-pro (~60 credits/10s). Check get_credit_balance first for expensive generations.

Risk signalsBulk/mass operation — affects multiple targets

Part of the Socialneuron server.

generate_video can modify Socialneuron 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 generate_video to create or modify resources in Socialneuron. 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 generate_video 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 Socialneuron.

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

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

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

Start an async AI video generation job \u2014 returns a job_id immediately. Poll with check_status every 10-30s until complete. Cost varies by model: veo3-fast (~15 credits/5s), kling-3 (~30 credits/5s), sora2-pro (~60 credits/10s). Check get_credit_balance first for expensive generations.. It is categorised as a Write tool in the Socialneuron MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.

How do I enforce a policy on generate_video? +

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

What risk level is generate_video? +

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

Can I rate-limit generate_video? +

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

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

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

Enforce policy on every Socialneuron tool call.

Deterministic rules across all 100 Socialneuron tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.

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