# CVE-2026-96804: MLflow statsmodels flavor arbitrary code execution via pickle deserialization

Source: https://junglewise.ai/threats/cve-2026-96804-mlflow-s-statsmodel-flavor-versions-2-1-0-to-3-14-0-omits-the  
Publisher: Junglewise Threat Intelligence, https://junglewise.ai/threats  
Published: 2026-09-23. Updated: 2026-09-26  
License: CC BY 4.0 with a link back

CVE-2026-96804 · Severity: high · CVSS 8.8 · Published 2026-09-23

MLflow is a platform for managing machine learning models. The statsmodels model flavor fails to enforce a security control that blocks unsafe pickle deserialization, allowing attackers to execute arbitrary code by uploading a malicious model file. An attacker with write access to a model repository can exploit this to gain code execution on systems loading the compromised model.

Technologies: [Mlflow](https://junglewise.ai/threats/technologies/mlflow-mlflow). Vendors: [MLflow](https://junglewise.ai/threats/vendors/mlflow).

## Quick answers

**Is CVE-2026-96804 exploited in the wild?** No. Junglewise Threat Intelligence has no report of CVE-2026-96804 being exploited in the wild.

**How severe is CVE-2026-96804?** High severity, with a CVSS score of 8.8, published 23 September 2026.

**Which products and versions are affected?** Mlflow 2.1.0 to 3.14.0.

**Is there a fix for CVE-2026-96804?** A patch was published on 2026-09-23: Patched in versions 3.15.0 and later for statsmodels flavor Junglewise finds every place you run it, drafts the upgrade and applies it once you approve.

**How is CVE-2026-96804 attacked?** It is attacked over the network, in a software library.

**Am I affected by CVE-2026-96804?** Check whether you run Mlflow at an affected version. Junglewise does that check for you around the clock: connect your code, cloud and domains, and it matches new vulnerabilities like this one against what you actually run and tells you when one reaches you.

## Technical details

The statsmodels flavor in MLflow's _load_model() function fails to check the MLFLOW_ALLOW_PICKLE_DESERIALIZATION flag before deserializing pickle files, allowing bypass of the intended security control. The vulnerability requires an attacker to have write access to the model storage location to craft a malicious MLmodel artifact. Exploitation results in arbitrary code execution in the context of the MLflow model loading process.

## Affected products

- MLflow MLflow 2.1.0 to 3.14.0

## Timeline

- 2026-09-16: disclosed
- 2026-09-23: patched: Patched in versions 3.15.0 and later for statsmodels flavor

## References

- [https://kb.cert.org/vuls/id/369093](https://kb.cert.org/vuls/id/369093)

## Related threats

- [MLflow path traversal in FileStore log outputs API](https://junglewise.ai/threats/cve-2026-127202-mlflow-before-3-18-0-using-the-filestore-backend-contains-a-path)
- [MLflow get-artifacts server-side request forgery information disclosure](https://junglewise.ai/threats/cve-2026-118628-mlflow-get-artifacts-server-side-request-forgery-information)
- [MLflow SHAP load_explainer deserialization of untrusted data](https://junglewise.ai/threats/cve-2026-118627-mlflow-shap-load-explainer-deserialization-of-untrusted-data)
- [MLflow Tracking Server missing authorization in get_endpoints](https://junglewise.ai/threats/cve-2026-118625-mlflow-tracking-server-missing-authorization-privilege)
- [MLflow Tracking Server authorization bypass in Jobs API](https://junglewise.ai/threats/cve-2026-118624-mlflow-tracking-server-jobs-missing-authorization-information)