GetAHOReadSetExportJob
AI agents call GetAHOReadSetExportJob to retrieve information from Amazon SageMaker AI MCP Server without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
The 'Get' prefix strongly suggests a query/retrieval action with no side effects. Even though the description is uninformative, the naming pattern aligns with Read operations that retrieve job metadata or status. No evidence suggests modification, execution, or deletion. Severity is low because retrieving export job information poses minimal risk if misused by an agent.
From the tool's definition Tool name prefix 'Get' indicates retrieval operation. 'AHOReadSet' and 'ExportJob' suggest querying status or details of an export job rather than creating, modifying, or deleting resources. Description is empty, reducing confidence slightly.
Attacks that exploit this kind of access
GetAHOReadSetExportJob. It is categorised as a Read tool in the Amazon SageMaker AI MCP Server MCP Server, which means it retrieves data without modifying state.
Register the Amazon SageMaker AI MCP Server MCP server in PolicyLayer and add a rule for GetAHOReadSetExportJob: 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.
GetAHOReadSetExportJob is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the GetAHOReadSetExportJob 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 GetAHOReadSetExportJob. 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.
GetAHOReadSetExportJob 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.