Advisories for Pypi/Dbt-Mcp package

DefaultUsageTracker.emit_tool_called_event() in src/dbt_mcp/tracking/tracking.py serializes the complete arguments dictionary of every MCP tool call and transmits it verbatim to the dbt Labs telemetry service via dbtlabs_vortex.producer.log_proto. No field is redacted, truncated, or excluded before transmission. This includes the sql_query parameter of the show tool (arbitrary SQL) and the vars parameter of run, build, and test (JSON string that may contain credentials). Telemetry is on by default; the opt-out mechanism requires explicit …

DbtMCP.call_tool() in src/dbt_mcp/mcp/server.py logs the complete raw arguments dictionary at INFO level on every tool invocation (line 67) and again at ERROR level if the call raises an exception (lines 77–79). No field is redacted before logging. When the documented DBT_MCP_SERVER_FILE_LOGGING=true feature is enabled, these log records are written to dbt-mcp.log in the project root directory as plaintext. Sensitive data — raw SQL queries, –vars payloads carrying credentials, node selectors …

_run_dbt_command() in src/dbt_mcp/dbt_cli/tools.py constructs the dbt subprocess argument list by appending user-supplied MCP tool parameters without sanitization. Two independent injection vectors exist. An MCP client can inject arbitrary dbt global flags — such as –profiles-dir, –project-dir, and –target — by crafting the node_selection string (Vector 1) or the resource_type JSON array (Vector 2). Because subprocess.Popen is called with shell=False and a list argument, shell metacharacter injection is not possible; however, …