CVE-2026-118627
Description
A flaw was found in MLflow. This vulnerability allows a remote attacker to execute arbitrary code by convincing a user to open a crafted file or load a malicious SHAP (SHapley Additive exPlanations) model. Because MLflow improperly validates user-supplied data during model deserialization, an attacker can run commands with the privileges of the user running the application.
Mitigation
--- CME Mitigation Guidance (2026-11-04) ---
Disclaimer: The following mitigation solutions are provided as a courtesy, pending the application of an official patch. This content was generated utilizing AI tools in conjunction with data from https://cmetaxonomy.org. Customers are advised to independently evaluate and test these temporary mitigations within their own infrastructure.
The highest-value action is to enforce type allowlisting and safe deserialization on model input streams during SHAP explainer model loading in MLflow load_explainer: upgrade to MLflow 3.17.0 (or the corresponding fixed Red Hat builds), which validates user-supplied data during SHAP explainer model loading to prevent deserialization of untrusted data, closing all 2 injection vectors (crafted SHAP explainer model file, malicious web page triggering model load). Until patched, this is a concern on MLflow users and environments loading SHAP explainer models across any platform, so Prohibit opening untrusted SHAP explainer model files and restrict visiting unverified web pages that trigger model loading. As a compensating measure, Deploy EDR agents to detect and terminate anomalous child processes or interactive shells spawned on the host and Use Falco or eBPF runtime security to detect suspicious process execution or outbound network connections triggered by deserialized model payloads. These controls limit blast radius and contain exploitation, but they do not fix the underlying untrusted deserialization defect — apply the upstream patch to fully remediate.
Highest-impact CME: CME-1302 Deserialization Allowlist (Safe Loading) (high)
Common Vulnerability Scoring System (CVSS) Score Details
Info alert:Important note
CVSS scores for open source components depend on vendor-specific factors (e.g. version or build chain). Therefore, Red Hat's score and impact rating can be different from NVD and other vendors. Red Hat remains the authoritative CVE Naming Authority (CNA) source for its products and services (see Red Hat classifications).
The following CVSS metrics and score provided are preliminary and subject to review.
CVSS v3 Score Breakdown
| Red Hat | NVD | cve.org | |
|---|---|---|---|
| Base Score | 7.8 | N/A | 7.8 |
| Attack Vector | Local | N/A | Local |
| Attack Complexity | Low | N/A | Low |
| Privileges Required | None | N/A | None |
| User Interaction | Required | N/A | Required |
| Scope | Unchanged | N/A | Unchanged |
| Confidentiality | High | N/A | High |
| Integrity Impact | High | N/A | High |
| Availability Impact | High | N/A | High |
Vector
Red Hat: CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H
cve.org: CVSS:3.0/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H
Understanding the Weakness (CWE)
Integrity
Technical Impact: Modify Application Data; Unexpected State
Attackers can modify unexpected objects or data that was assumed to be safe from modification. Deserialized data or code could be modified without using the provided accessor functions, or unexpected functions could be invoked.
Availability
Technical Impact: DoS: Resource Consumption (CPU)
If a function is making an assumption on when to terminate, based on a sentry in a string, it could easily never terminate.
Other
Technical Impact: Varies by Context
The consequences can vary widely, because it depends on which objects or methods are being deserialized, and how they are used. Making an assumption that the code in the deserialized object is valid is dangerous and can enable exploitation. One example is attackers using gadget chains to perform unauthorized actions, such as generating a shell.
Frequently Asked Questions
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