mlflow
: Remote code execution on unauthenticated MLflow servers via third-party scorer deserialization importing caller-placed artifact modules
Not on CISA's list of exploited flaws, and not in the news we track.
CVE-2026-116692Critical9.8
Black Shard's team can check your exposure to CVE-2026-116692 and take the fix off your plate.
MLflow is an open source AI engineering platform for agents, large language models, and machine learning models. From 3.12.0rc0 until 3.17.0, third-party scorer deserialization in mlflow/genai/scorers/base.py accepts request-selected descendants of THIRD_PARTY_SCORER_ALLOWED_MODULES rather than requiring an exact allowed module. A caller can use MLflow artifact endpoints to place a Python module beneath a writable allowed package and submit its module and class through /ajax-api/3.0/mlflow/scorer/invoke. Scorer.model_validate_json() imports the module and constructs the class before trace processing. Authentication-disabled deployments permit this code execution without credentials, while basic-auth deployments require a principal capable of creating an experiment and run and uploading artifacts. In either configuration, the allowed scorer package must be writable by the server process. This issue is fixed in version 3.17.0.
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Chance of exploitation in the next 30 days
0.70%
EPSS score from FIRST
Updated 13 Nov 2026, 12:02 UTC, 30 of 36 sources answering.
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