AI agents use tdarr_auth_logout to create or update resources in Tdarr — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Tdarr environment.
Logging out a user invalidates the current session, which is a reversible state change (the user can log back in). It doesn't delete data, execute code, or have financial implications. It modifies authentication state, fitting the Write category. Severity is low since the blast radius is limited to session termination.
From the tool's definition Log out the current user
Attacks that exploit this kind of access
Log out the current user. It is categorised as a Write tool in the Tdarr MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the Tdarr MCP server in PolicyLayer and add a rule for tdarr_auth_logout: 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 Tdarr. Nothing to install.
tdarr_auth_logout is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.
Yes. Add a rate_limit block to the tdarr_auth_logout 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 tdarr_auth_logout. 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.
tdarr_auth_logout is provided by the Tdarr MCP server (maximeallanic/tdarr-mcp). 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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