CVE-2026-4035— MLflow 安全漏洞

Environment Variable Resolution Vulnerability in mlflow/mlflow

A vulnerability in mlflow/mlflow versions prior to 3.11.0 allows for the resolution of environment variables in AI Gateway secrets, which can be exploited to exfiltrate sensitive server-side environment credentials to an attacker-controlled endpoint. This issue arises because the `api_key` field in gateway secrets can accept `$ENV_VAR` references, which are resolved against the MLflow server's environment during runtime. The resolved secrets are then sent in provider authentication headers to the configured upstream `api_base`. This vulnerability can be exploited by low-privileged authenticated users in basic-auth deployments or by unauthenticated users in default deployments without `basic-auth`. The impact includes potential leakage of sensitive credentials such as cloud artifact credentials (`AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`), which could lead to artifact poisoning and cross-boundary code execution in downstream environments. The issue is fixed in version 3.11.0.

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通过发送数据的信息暴露

MLflow 安全漏洞

MLflow是MLflow开源的一个简化机器学习开发的平台，包括跟踪实验、将代码打包成可重复的运行以及共享和部署模型。 MLflow 3.11.0之前版本存在安全漏洞，该漏洞源于AI Gateway secrets中环境变量解析问题，可能导致敏感凭据泄露。

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