CVE-2026-118623
Description
A flaw was found in MLflow. This vulnerability allows an attacker to execute arbitrary code in the context of the running application by tricking a user into opening a crafted file or visiting a malicious webpage. The issue stems from the unsafe deserialization (reconstructing saved data objects) of untrusted data when parsing pickle evaluation artifacts without proper validation.
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 restrict deserialization in PickleEvaluationArtifact to safe loaders or strictly allowlisted classes to prevent code execution during artifact unpickling: upgrade to MLflow 3.13.0 (or the corresponding fixed Red Hat builds), which validates user-supplied evaluation artifact data to eliminate unsafe deserialization of untrusted pickle streams, closing all 2 injection vectors (crafted pickle artifact file, malicious webpage triggering pickle data parsing). Until patched, this is a concern on Systems running MLflow where users interact with evaluation artifact files or web endpoints, so Prevent users from opening untrusted pickle evaluation artifact files or visiting untrusted web links that trigger MLflow artifact loading. As a compensating measure, Enforce a read-only root filesystem on containerized MLflow workloads to prevent payloads from writing binaries or establishing filesystem persistence, Deploy EDR agents to detect anomalous child process execution and payload activity spawned within the MLflow process context, and Configure Falco or eBPF runtime security to monitor and alert on unauthorized process spawns or unexpected outbound connections. These controls limit blast radius and contain exploitation, but they do not fix the underlying untrusted deserialization flaw — 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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