MLflow: Environment variable injection in AI Gateway secrets enables server-side credential exfiltration

HIGH 7.7CVE-2026-4035 · BIT-mlflow-2026-4035 · GHSA-g35p-px32-whv6 · Published 03/06/2026 · updated 14/08/2026

Package	Affected versions	Patched versions
mlflow PyPI	`< 3.11.0`	`3.11.0`
mlflow lfprojects	`< 3.11.0`	`3.11.0`

Description

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.

NVD description

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.

References

nvd.nist.gov/vuln/detail/CVE-2026-4035 · ADVISORY
mlflow/mlflow commit 4a3f2f7 · WEB
access.redhat.com/security/cve/CVE-2026-4035 · WEB
bugzilla.redhat.com/show_bug.cgi · WEB
mlflow/mlflow · PACKAGE
huntr.com/bounties/f8e591a0-0f19-4910-b82e-16c9956f2233 · WEB
security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-4035.json · WEB
GitHub Advisory GHSA-g35p-px32-whv6 · ADVISORY

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Package

Affected versions

Patched versions

mlflow

PyPI

< 3.11.0

3.11.0

mlflow

lfprojects

< 3.11.0

3.11.0

Description

NVD description

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.