# CVE-2026-118628: MLflow get-artifacts server-side request forgery information disclosure

Source: https://junglewise.ai/threats/cve-2026-118628-mlflow-get-artifacts-server-side-request-forgery-information  
Publisher: Junglewise Threat Intelligence, https://junglewise.ai/threats  
Published: 2026-11-04. Updated: 2026-11-04  
License: CC BY 4.0 with a link back

CVE-2026-118628 · Severity: high · CVSS 8.2 · Published 2026-11-04

MLflow is an open-source platform for managing machine learning experiments and models. This vulnerability allows unauthenticated remote attackers to exploit improper URI validation in the get-artifacts function to disclose sensitive information from the server, potentially exposing credentials, configuration data, or other confidential files.

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

## Quick answers

**Is CVE-2026-118628 exploited in the wild?** No. Junglewise Threat Intelligence has no report of CVE-2026-118628 being exploited in the wild.

**How severe is CVE-2026-118628?** High severity, with a CVSS score of 8.2, published 4 November 2026.

**Which products and versions are affected?** Mlflow prior to 3.17.0.

**Is there a fix for CVE-2026-118628?** Yes: fixed in MLflow 3.17.0. Junglewise finds every place you run it, drafts the upgrade and applies it once you approve.

**How is CVE-2026-118628 attacked?** It is attacked over the network, in a software library.

**Am I affected by CVE-2026-118628?** 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.

## How to fix

- Fixed in MLflow 3.17.0

## Technical details

The get-artifacts function in MLflow fails to properly validate URIs before accessing resources, enabling a server-side request forgery (SSRF) attack. An unauthenticated attacker can exploit this over the network without user interaction to read arbitrary files or data accessible to the MLflow server process, resulting in information disclosure with partial integrity impact.

## Affected products

- MLflow MLflow prior to 3.17.0

## Timeline

- 2026-02-05: disclosed: Vulnerability reported to vendor
- 2026-10-29: advisory: Coordinated public release of advisory

## References

- [https://www.zerodayinitiative.com/advisories/ZDI-26-844/](https://www.zerodayinitiative.com/advisories/ZDI-26-844/)

## 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 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)
- [MLflow PickleEvaluationArtifact deserialization of untrusted data](https://junglewise.ai/threats/cve-2026-118623-mlflow-pickleevaluationartifact-deserialization-of-untrusted)