Stored XSS via unsafe YAML parsing in MLflow

published 2026-04-07 7 mo ago modified 2026-09-10 1 mo ago also BIT-mlflow-2026-33865 , EUVD-2026-19608 , GHSA-fh64-r2vc-xvhr , PYSEC-2026-93

cvss: 5.4; version 3.1, NVD
cisa kev: Not listed; not in CISA's catalog
epss: 0.2%; EPSS 0.2% chance of exploitation in 30 days, percentile 13%
public exploits: 0; none known

MLflow is vulnerable to Stored Cross-Site Scripting (XSS) caused by unsafe parsing of YAML-based MLmodel artifacts in its web interface. An authenticated attacker can upload a malicious MLmodel file containing a payload that executes when another user views the artifact in the UI. This allows actions such as session hijacking or performing operations on behalf of the victim.

This issue affects MLflow version through 3.10.1

Patch / fix

fixed in 2 links 2

pypi mlflow 3.11.0rc0, 3.11.1
bitnami mlflow 3.11.1

github.com/mlflow/mlflow@aca4dd0 commit
github.com/mlflow/mlflow#21435 pull request

Vendor advisories

What the vendors themselves say about their products: affected, fixed or not affected, and what to do.

red hat product security vex CVE-2026-33865 updated 2026-07-05 cvss 4.6

MLflow: MLflow: Stored Cross-Site Scripting (XSS) via unsafe parsing of MLmodel artifacts

Red Hat OpenShift AI (RHOAI) rhoai/odh-mlflow-rhel9, rhoai/odh-th06-cpu-torch210-py312-rhel9, rhoai/odh-th06-rocm64-torch291-py312-rhel9, rhoai/odh-th06-cuda130-torch210-py312-rhel9, rhoai/odh-training-cuda128-torch29-py312-rhel9, rhoai/odh-pipeline-runtime-pytorch-cuda-py312-rhel9, +11 more
affected: workaround, no fix available “Fix deferred”
Red Hat OpenShift AI (RHOAI) rhoai/odh-pipeline-runtime-tensorflow-rocm-py312-rhel9, rhoai/odh-workbench-jupyter-tensorflow-rocm-py312-rhel9
not affected: vulnerable code not present

Red Hat CSAF VEX © Red Hat, Inc., from https://security.access.redhat.com/data/csaf/v2/vex/; changed: reduced to product statuses and remediations. CC BY 4.0 · csaf

CISA SSVC

Exploitation: Proof of concept is public
Automatable: no
Technical impact: partial
Assessed: 6 mo ago (2026-04-14)

Exploit prediction

0.23% chance of exploitation in the next 30 days, higher than 13% of all scored CVEs (FIRST EPSS, 2026-11-08).

Exploits and checks

afine.com https://afine.com/blogs/attacking-mlflow-how-ml-artifacts-become-attack-vectors referenced PoC, tagged "exploit" by a source, unverified

CVSS scores

assigned by	version	score	vector
CNA	4.0	5.1	CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:P/VC:N/VI:L/VA:N/SC:L/SI:L/SA:N
GitHub	3.1	5.4	CVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:C/C:L/I:L/A:N
CNA	4.0	5.1	CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:P/VC:N/VI:L/VA:N/SC:L/SI:L/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X
NVD	3.1	5.4	CVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:C/C:L/I:L/A:N
OSV	3.1	5.4	CVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:C/C:L/I:L/A:N

Affected packages

ecosystem	package	affected	fixed in
pypi	mlflow	<3.11.1 <3.11.0rc0	3.11.1 3.11.0rc0
bitnami	mlflow	<3.11.1	3.11.1

Affected products

Mlflow Mlflow

Classification

Weakness

CWE-79 Improper Neutralization of Input During Web Page Generation

References

github.com /mlflow/mlflow/pull/21435
patch, web, issue-tracking
github.com /mlflow/mlflow/commit/aca4dd0ec88a12f7655155c224371280e9b45dda
web
afine.com /blogs/attacking-mlflow-how-ml-artifacts-become-attack-vectors
exploit, technical-description, web, third-party-advisory, advisory, evidence
cert.pl /en/posts/2026/04/CVE-2026-33865/
third-party-advisory, advisory
nvd.nist.gov /vuln/detail/CVE-2026-33865
advisory, web
cert.pl /en/posts/2026/04/CVE-2026-33865
web
github.com /mlflow/mlflow
package
github.com /pypa/advisory-database/tree/main/vulns/mlflow/PYSEC-2026-93.yaml
web
github.com /advisories/GHSA-fh64-r2vc-xvhr
advisory

Sources

Fencer Vulns keeps one entry per vulnerability: the CVE id wins, then GHSA, and records that alias each other are merged, except that two CVEs always keep separate entries. The same data is available as JSON.

Summary