AI Threat Alert
CVE-2025-15036: MLflow: path traversal enables sandbox escape, file overwrite
GHSA-vhcx-3pq2-4fvc CRITICAL CISA: ATTENDMLflow versions before v3.7.0 allow an attacker who controls a tar.gz artifact to overwrite arbitrary files and potentially escape the sandbox in multi-tenant or shared cluster environments (e.g., Databricks Connect). If your ML platform ingests user-supplied or third-party model artifacts via MLflow, treat this as HIGH severity and patch to v3.7.0 immediately. In shared environments where multiple teams or tenants load artifacts, this is a lateral movement and privilege escalation vector—prioritize accordingly.
What is the risk?
Although no CVSS score has been assigned yet, this vulnerability carries high effective risk in enterprise MLOps environments. The exploit primitive—crafting a malicious tar.gz—is trivial and well-documented. Impact is severe in multi-tenant ML platforms where sandbox isolation is a security boundary. Single-tenant, air-gapped deployments where artifact provenance is tightly controlled face lower—but not zero—risk. The absence of CISA KEV listing and EPSS score reflects recency, not low severity. Shared cluster environments using Databricks Connect are the highest-risk deployment pattern.
What systems are affected?
| Package | Ecosystem | Vulnerable Range | Patched |
|---|---|---|---|
| mlflow | pip | < 3.9.0rc0 | 3.9.0rc0 |
Do you use mlflow? You're affected.
Severity & Risk
Attack Surface
What should I do?
5 steps-
PATCH
Upgrade MLflow to v3.7.0 or later immediately—this is the definitive fix. Reference commit: 3bf6d81ac4d38654c8ff012dbd0c3e9f17e7e346.
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VERIFY PROVENANCE
Until patched, enforce strict provenance controls on all tar.gz artifacts ingested by MLflow—only accept artifacts from trusted, cryptographically signed sources.
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RESTRICT ARTIFACT SOURCES
Disable ingestion of externally-sourced model artifacts in multi-tenant clusters if patching is delayed.
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DETECT
Search logs for unexpected file writes outside expected MLflow working directories during artifact extraction. Monitor for new files created in /etc, /usr, or home directories during MLflow model load operations.
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AUDIT DEPLOYMENTS
Identify all Databricks Connect and MLflow deployments using
dbconnect_artifact_cache—prioritize shared/multi-tenant clusters.
CISA SSVC Assessment
Source: CISA Vulnrichment (SSVC v2.0). Decision based on the CISA Coordinator decision tree.
Classification
Compliance Impact
This CVE is relevant to:
Frequently Asked Questions
What is CVE-2025-15036?
MLflow versions before v3.7.0 allow an attacker who controls a tar.gz artifact to overwrite arbitrary files and potentially escape the sandbox in multi-tenant or shared cluster environments (e.g., Databricks Connect). If your ML platform ingests user-supplied or third-party model artifacts via MLflow, treat this as HIGH severity and patch to v3.7.0 immediately. In shared environments where multiple teams or tenants load artifacts, this is a lateral movement and privilege escalation vector—prioritize accordingly.
Is CVE-2025-15036 actively exploited?
No confirmed active exploitation of CVE-2025-15036 has been reported, but organizations should still patch proactively.
How to fix CVE-2025-15036?
1. PATCH: Upgrade MLflow to v3.7.0 or later immediately—this is the definitive fix. Reference commit: 3bf6d81ac4d38654c8ff012dbd0c3e9f17e7e346. 2. VERIFY PROVENANCE: Until patched, enforce strict provenance controls on all tar.gz artifacts ingested by MLflow—only accept artifacts from trusted, cryptographically signed sources. 3. RESTRICT ARTIFACT SOURCES: Disable ingestion of externally-sourced model artifacts in multi-tenant clusters if patching is delayed. 4. DETECT: Search logs for unexpected file writes outside expected MLflow working directories during artifact extraction. Monitor for new files created in /etc, /usr, or home directories during MLflow model load operations. 5. AUDIT DEPLOYMENTS: Identify all Databricks Connect and MLflow deployments using `dbconnect_artifact_cache`—prioritize shared/multi-tenant clusters.
What systems are affected by CVE-2025-15036?
This vulnerability affects the following AI/ML architecture patterns: Training pipelines, MLOps platforms, Model serving, Multi-tenant ML environments, CI/CD ML pipelines.
What is the CVSS score for CVE-2025-15036?
CVE-2025-15036 has a CVSS v3.1 base score of 9.6 (CRITICAL). The EPSS exploitation probability is 0.04%.
Technical Details
NVD Description
A path traversal vulnerability exists in the `extract_archive_to_dir` function within the `mlflow/pyfunc/dbconnect_artifact_cache.py` file of the mlflow/mlflow repository. This vulnerability, present in versions before v3.7.0, arises due to the lack of validation of tar member paths during extraction. An attacker with control over the tar.gz file can exploit this issue to overwrite arbitrary files or gain elevated privileges, potentially escaping the sandbox directory in multi-tenant or shared cluster environments.
Exploitation Scenario
An attacker operating in a multi-tenant ML environment (e.g., a shared Databricks workspace) crafts a malicious tar.gz file containing path traversal sequences in member filenames (e.g., `../../etc/cron.d/backdoor`). They publish this as a model artifact to a shared MLflow Model Registry—either by compromising a low-privilege account, exploiting a misconfigured registry, or via a poisoned upstream dependency. When a victim user or automated pipeline loads the model via `mlflow.pyfunc.load_model()`, MLflow's artifact cache mechanism extracts the tar.gz without validating member paths. The malicious file is written outside the sandbox directory, potentially overwriting a cron job, SSH authorized_keys, or Python site-packages to achieve persistent code execution with elevated privileges on the shared cluster.
Weaknesses (CWE)
CVSS Vector
CVSS:3.0/AV:N/AC:L/PR:N/UI:R/S:C/C:H/I:H/A:H References
Timeline
Related Vulnerabilities
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