Amin Ghadersohi 76ad5e1bf7 fix(mcp): validate API keys via FastMCP AccessToken and lock down ApiKey perms
Three independent bugs let MCP requests presenting Bearer tokens with the
sst_ prefix authenticate as MCP_DEV_USERNAME without any validation under
streamable-http:

1. _resolve_user_from_api_key read the token from flask.request.headers,
   but the streamable-http transport never pushes a Flask request context
   — has_request_context() was always False, so the function returned
   None before validating, falling through to the dev-user fallback.
   Now reads the token from FastMCP's per-request AccessToken (which the
   CompositeTokenVerifier already populated) and fails closed when the
   key is invalid.

2. CompositeTokenVerifier was only installed when MCP_AUTH_ENABLED=True.
   With FAB_API_KEY_ENABLED=True alone, no transport-level verifier
   existed at all. The factory now builds an API-key-only verifier in
   that case (jwt_verifier=None) that rejects non-API-key Bearer tokens
   at the transport instead of silently accepting them.

3. The pass-through AccessToken was minted with scopes=[], which would
   make FastMCP's RequireAuthMiddleware 403 every API-key request when
   MCP_REQUIRED_SCOPES is non-empty. Pass-through now propagates
   self.required_scopes.

Also addresses Daniel's review comment on superset/security/manager.py:
adds "ApiKey" to ADMIN_ONLY_VIEW_MENUS so the FAB ApiKeyApi PVMs are
gated to Admin instead of leaking to Alpha and Gamma.

Renames the pass-through claim from _api_key_passthrough to the
namespaced _superset_mcp_api_key_passthrough (exported as
API_KEY_PASSTHROUGH_CLAIM) so a custom claim from an external IdP can't
accidentally divert a JWT into the API-key validation path.

Tests updated to mock get_access_token instead of app.test_request_context
(the simulated Flask context was the reason the prior tests passed while
production failed). New tests cover API-key-only verifier mode, scope
propagation on pass-through, and the namespaced-claim isolation.
2026-05-08 14:26:05 -04:00
2026-04-17 17:21:23 -03:00
2025-12-04 13:18:34 -05:00
2020-03-25 22:00:41 -07:00
2024-04-15 11:21:42 -06:00

Superset

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A modern, enterprise-ready business intelligence web application.

Documentation

  • User Guide — For analysts and business users. Explore data, build charts, create dashboards, and connect databases.
  • Administrator Guide — Install, configure, and operate Superset. Covers security, scaling, and database drivers.
  • Developer Guide — Contribute to Superset or build on its REST API and extension framework.

Why Superset? | Supported Databases | Release Notes | Get Involved | Resources | Organizations Using Superset

Why Superset?

Superset is a modern data exploration and data visualization platform. Superset can replace or augment proprietary business intelligence tools for many teams. Superset integrates well with a variety of data sources.

Superset provides:

  • A no-code interface for building charts quickly
  • A powerful, web-based SQL Editor for advanced querying
  • A lightweight semantic layer for quickly defining custom dimensions and metrics
  • Out of the box support for nearly any SQL database or data engine
  • A wide array of beautiful visualizations to showcase your data, ranging from simple bar charts to geospatial visualizations
  • Lightweight, configurable caching layer to help ease database load
  • Highly extensible security roles and authentication options
  • An API for programmatic customization
  • A cloud-native architecture designed from the ground up for scale

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Video Overview

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Large Gallery of Visualizations


Craft Beautiful, Dynamic Dashboards


No-Code Chart Builder


Powerful SQL Editor


Supported Databases

Superset can query data from any SQL-speaking datastore or data engine (Presto, Trino, Athena, and more) that has a Python DB-API driver and a SQLAlchemy dialect.

Here are some of the major database solutions that are supported:

Amazon Athena   Amazon DynamoDB   Amazon Redshift   Apache Doris   Apache Drill   Apache Druid   Apache Hive   Apache Impala   Apache Kylin   Apache Pinot   Apache Solr   Apache Spark SQL   Ascend   Aurora MySQL (Data API)   Aurora PostgreSQL (Data API)   Azure Data Explorer   Azure Synapse   ClickHouse   Cloudflare D1   CockroachDB   Couchbase   CrateDB   Databend   Databricks   Denodo   Dremio   DuckDB   Elasticsearch   Exasol   Firebird   Firebolt   Google BigQuery   Google Sheets   Greenplum   Hologres   IBM Db2   IBM Netezza Performance Server   MariaDB   Microsoft SQL Server   MonetDB   MongoDB   MotherDuck   OceanBase   Oracle   Presto   RisingWave   SAP HANA   SAP Sybase   Shillelagh   SingleStore   Snowflake   SQLite   StarRocks   Superset meta database   TDengine   Teradata   TimescaleDB   Trino   Vertica   YDB   YugabyteDB

A more comprehensive list of supported databases along with the configuration instructions can be found here.

Want to add support for your datastore or data engine? Read more here about the technical requirements.

Installation and Configuration

Try out Superset's quickstart guide or learn about the options for production deployments.

Get Involved

Contributor Guide

Interested in contributing? Check out our Developer Guide to find resources around contributing along with a detailed guide on how to set up a development environment.

Resources

Understanding the Superset Points of View

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