Vulnerabilities

MLflow versions prior to 3.15.0 contain a server-side request forgery flaw (CWE-918) in the unauthenticated POST /api/2.0/mlflow/webhooks/{id}/test endpoint. The webhook URL is validated only for the original request, but delivery follows redirects and re-resolves the hostname without pinning the validated address, letting an attacker redirect server-side requests to internal network services or cloud instance metadata endpoints. Because the endpoint returns response_status and response_body, attackers can read internal service responses and, per public reporting, steal cloud credentials and secrets from metadata services. Any self-hosted MLflow deployment is affected, with internet-exposed servers at the highest risk. Exploitation is confirmed in the wild: CISA added the flaw to its Known Exploited Vulnerabilities catalog on 2026-08-19, and headlines report active attacks stealing cloud credentials; EPSS puts 30-day exploitation probability at 16.4% (97th percentile).

Do: Upgrade to MLflow 3.15.0 or later immediately, prioritizing any MLflow server reachable from the internet. Until patched, restrict network access to MLflow (especially the /api/2.0/mlflow/webhooks/{id}/test endpoint), place it behind authentication or a reverse proxy, and review webhook logs for unexpected test requests and cloud metadata endpoint access; rotate cloud credentials and secrets if compromise indicators are found. Federal agencies must apply vendor mitigations in accordance with CISA BOD 26-04 guidance or discontinue use if mitigations are unavailable.

The MCP Python SDK, called mcp on PyPI, is a Python implementation of the Model Context Protocol (MCP). From 1.23.0 until 1.27.2, default handlers installed by server.experimental.enable_tasks() for tasks/list, tasks/get, tasks/result, and tasks/cancel operate only on task identifiers without recording the session that created each task, allowing any connected client to enumerate, read results from, consume messages for, or cancel other clients' tasks. This issue is fixed in version 1.27.2.

In MLflow versions prior to 3.14.0, when running with authentication enabled, the trace API endpoints lack proper authorization validators. This allows any authenticated user to bypass experiment-level authorization controls on all trace operations, including reading, deleting, and modifying traces on experiments they do not have permission to access. The issue arises from the `_before_request` handler, which does not register authorization validators for trace endpoints, resulting in requests proceeding without validation. This vulnerability can expose sensitive data, destroy audit logs, and allow unauthorized modifications.

A vulnerability has been found in MLflow up to 4666cffc7912ea606d592fc38d6a75e2935f65e7. The impacted element is an unknown function of the component Experiment-scoped Label Schema CRUD API. Such manipulation leads to missing authorization. It is possible to launch the attack remotely. A high complexity level is associated with this attack. The exploitability is regarded as difficult. The exploit has been disclosed to the public and may be used. A reply to the GitHub issue explains, that "[t]he labeling schema PR has not been merged yet. The auth handlers will be added before the release."