AI agents call get_issues 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 or queries data from a Mantis issue tracking system with optional filtering. It has no side effects—it does not create, modify, delete, or execute operations. The mention of 'select' and field selection further confirms it is a read-only data retrieval operation.
From the tool's definition Tool name is 'get_issues' and description states '獲取 Mantis 問題列表' (retrieve Mantis issue list) with filtering capabilities.
Attacks that exploit this kind of access
獲取 Mantis 問題列表，可根據多個條件進行過濾，建議查詢時select選擇id,summary,description就好，資訊過多可能導致程式異常. 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_issues: 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_issues 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_issues 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_issues. 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_issues 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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