AI agents call get_user to retrieve information from Mantis without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool retrieves user information from Mantis based on a username lookup. It is a read-only query operation that returns data without creating, modifying, deleting, or executing any actions. The low severity reflects minimal risk even if misused—disclosure of user information in a bug tracking system is typically low-sensitivity data.
From the tool's definition Tool name is 'get_user' and description states '根據用戶名稱查詢 Mantis 用戶' (Query Mantis users by username). The verb 'get' and 'query' indicate data retrieval with no modification or side effects.
Attacks that exploit this kind of access
根據用戶名稱查詢 Mantis 用戶. It is categorised as a Read tool in the Mantis MCP Server, which means it retrieves data without modifying state.
Register the Mantis MCP server in PolicyLayer and add a rule for get_user: 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 Mantis. Nothing to install.
get_user 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 get_user 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 get_user. 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.
get_user is provided by the Mantis MCP server (mantis-mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Every MCP server has a record like this.
Type a name, get the same breakdown: verified identity, auth posture, risk grade, capabilities, recommended policy.
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