AI agents invoke StartAHORunBatch to trigger actions in Amazon SageMaker AI MCP Server. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call — builds kicked off, notifications sent, workflows started.
A tool that starts a batch run triggers external operations and processes whose side effects depend on the arguments provided (batch configuration, data, parameters). This fits the Execute category—it runs or triggers operations whose consequences are determined by input. Without evidence of data deletion or financial impact, Execute is the appropriate severity level.
From the tool's definition Tool name 'StartAHORunBatch' implies initiating a batch operation. The 'Start' verb and 'Batch' noun suggest triggering an external operation whose effects depend on how the batch is configured.
Documented attack patterns abuse exactly the kind of access StartAHORunBatch gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Amazon SageMaker AI MCP Server, and nothing reaches the server without passing your rules. This is the rule we recommend for StartAHORunBatch:
{
"version": "1",
"default": "deny",
"tools": {
"StartAHORunBatch": {
"limits": [
{
"counter": "startahorunbatch_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}StartAHORunBatch stays usable, but rate-capped — a runaway agent can't fire it dozens of times a minute. Everything else on the server is denied unless you say otherwise.
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StartAHORunBatch. It is categorised as a Execute tool in the Amazon SageMaker AI MCP Server MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Amazon SageMaker AI MCP Server MCP server in PolicyLayer and add a rule for StartAHORunBatch: 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 Amazon SageMaker AI MCP Server. Nothing to install.
StartAHORunBatch is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the StartAHORunBatch 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.
Set action: deny in the PolicyLayer policy for StartAHORunBatch. 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.
StartAHORunBatch is provided by the Amazon SageMaker AI MCP Server MCP server (awslabs.sagemaker-ai-mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Amazon SageMaker AI MCP Server, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
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