Track ML experiments, manage model registry with versioning, deploy models to production, and reproduce experiments with MLflow - framework-agnostic ML lifecycle platform
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1 high severity finding. You should review these findings carefully before considering using this skill.
The skill handles credentials insecurely by requiring the agent to include secret values verbatim in its generated output. This exposes credentials in the agent’s context and conversation history, creating a risk of data exfiltration.
The skill includes a command-line example embedding a database URI with "user:password" in the --backend-store-uri argument, an insecure pattern that would encourage copying real credentials verbatim into CLI commands and therefore risks secret exfiltration.
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