CVE-2026-4035: 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.
Security readout for executives and security teams
Plain-English summary
MLflow before 3.11.0 can reveal secrets stored in its server environment. An attacker able to configure AI Gateway secrets may cause MLflow to resolve an environment-variable reference and send the resulting credential to an attacker-controlled service. Exposed cloud credentials could enable artifact tampering and potentially affect downstream systems.
Executive priority
Treat this as an immediate remediation item for exposed or shared MLflow services. Prioritize upgrading unauthenticated deployments and instances holding cloud or artifact credentials. If suspicious configuration or outbound traffic is found, begin credential rotation and investigate downstream artifact integrity.
Technical view
The AI Gateway accepted environment-variable references in the api_key field, resolved them against the MLflow server environment, and transmitted the resolved value in authentication headers to the configured api_base. Exposure requires no authentication in default deployments without basic-auth, or only low privileges when basic-auth is enabled. Version 3.11.0 fixes the issue.
Likely exposure
Internet- or broadly network-accessible MLflow deployments below 3.11.0 are most exposed, especially those using the AI Gateway and lacking basic-auth. Basic-auth reduces the attacker pool but remains vulnerable to low-privileged authenticated users. Impact depends on which credentials exist in the server environment and their permissions.
Exploitation context
The supplied record reports no CISA KEV listing and provides no evidence of active exploitation. Exploitation is nevertheless plausible because it requires low complexity, network access, and limited or no privileges depending on authentication configuration. The CVSS score is 9.1, with confidentiality as the primary impact.
Researcher notes
CWE-201 reflects unintended exposure of sensitive information. The described cross-boundary code-execution risk is indirect: leaked cloud credentials may permit artifact poisoning, which could later affect systems consuming those artifacts. The source bundle does not establish direct code execution through MLflow or exploitation in the wild.
Mitigation direction
Upgrade MLflow to version 3.11.0 or later.
Until upgraded, restrict AI Gateway configuration access to trusted administrators.
Constrain MLflow's outbound connections to approved provider endpoints where operationally feasible.
Rotate exposed or potentially exposed environment credentials and reduce their permissions.
Validation and detection
Confirm every MLflow instance reports version 3.11.0 or later.
Determine whether each deployment enables basic-auth and who can configure AI Gateway secrets.
Review gateway configurations for environment-variable references and unexpected api_base destinations.
Inspect relevant logs for suspicious gateway changes or outbound requests to unapproved endpoints.
Identify server environment credentials and assess whether rotation is required.
Generated from the cited source records. This long-tail analysis has not been individually reviewed by a named human.
Potential ATT&CK relevance
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cwe · medium confidence lookup
CWE-201: Information exposure and cloud metadata lookup
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CWE-201 · source CWE mapping
Insertion of Sensitive Information Into Sent Data
Insertion of Sensitive Information Into Sent Data represents a recurring weakness pattern that can create exploitable paths when design, validation, or implementation controls are missing.