CVE-2026-4035 (GCVE-0-2026-4035)
Vulnerability from cvelistv5 – Published: 2026-06-03 07:18 – Updated: 2026-08-14 12:04
VLAI
EPSS
VEX
Title
Environment Variable Resolution Vulnerability in mlflow/mlflow
Summary
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.
Severity
9.1 (Critical)
7.7 (High)
SSVC
Exploitation: poc
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-06-03 13:10 UTC
CWE
- CWE-201 - Insertion of Sensitive Information Into Sent Data
Assigner
References
5 references
| URL | Tags |
|---|---|
| https://huntr.com/bounties/f8e591a0-0f19-4910-b82… | |
| https://github.com/mlflow/mlflow/commit/4a3f2f720… | |
| https://access.redhat.com/security/cve/CVE-2026-4035 | vdb-entryx_refsource_REDHAT |
| https://bugzilla.redhat.com/show_bug.cgi?id=2484318 | issue-trackingx_refsource_REDHAT |
| https://security.access.redhat.com/data/csaf/v2/v… | x_sadp-csaf-vex |
Impacted products
2 products
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| mlflow | mlflow/mlflow |
Affected:
unspecified , < 3.11.0
(custom)
|
guessed | |
| Red Hat | Red Hat OpenShift AI (RHOAI) |
cpe:/a:redhat:openshift_ai
|
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.
Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or observed by the user.
- Confirmed: The vulnerability has been validated from an analyst's perspective.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
- Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
- Not confirmed: The user expressed doubt about the validity of the vulnerability.
- Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
model, served locally by ML-Gateway.
They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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Related by attack behaviour
Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.
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