AI agents call model_info to retrieve information from Litellm without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool queries and returns metadata about a model. It does not create, modify, delete, or execute anything; it is purely informational. The low severity reflects minimal blast radius even if misused—an agent retrieving model details poses no direct harm.
From the tool's definition Tool name is 'model_info' and description states 'Get detailed information about a specific model' — a retrieval operation with no side effects.
Attacks that exploit this kind of access
Get detailed information about a specific model. It is categorised as a Read tool in the Litellm MCP Server, which means it retrieves data without modifying state.
Register the Litellm MCP server in PolicyLayer and add a rule for model_info: 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 Litellm. Nothing to install.
model_info 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 model_info 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 model_info. 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.
model_info is provided by the Litellm MCP server (litellm-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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