MLflow is an open-source platform for managing the machine learning lifecycle. Teams use it to record experiments, compare runs, store model artifacts, and track which code, data, and parameters produced a result. In practice, it often includes a tracking server, artifact storage, and integrations with training and deployment systems.
MLflow matters in cyber security because the tracking server may sit near sensitive infrastructure and credentials. If it is exposed or misconfigured, an attacker may try to abuse it as a pivot point into internal networks, cloud metadata services, or storage backends. Defenders reduce that risk by limiting network exposure, enforcing authentication, validating outbound requests, segmenting cloud permissions, and monitoring for unusual server-side access patterns.