CVE-2026-79721
Description
A flaw was found in MLflow. This vulnerability allows a remote attacker to achieve arbitrary code execution on an end user's system. This occurs when a maliciously crafted model artifact is loaded by the MLflow platform, enabling the execution of unauthorized code.
Statement
This vulnerability is rated as having an Important impact rather than Critical because triggering the flaw requires an end user or automated job to ingest an untrusted model, preventing unprompted remote exploitation. In Red Hat OpenShift AI (RHOAI), MLflow is integrated across containerized workbenches and pipeline runtimes to manage machine learning workflows and experiment tracking. If an untrusted model package is loaded, any resulting code execution is confined to the privileges and security context of the specific container instance.
Mitigation
Avoid loading, logging, or serving untrusted model artifacts from unverified or public repositories within Red Hat OpenShift AI workbenches and pipelines. Ensure all ingested models originate from trusted, authenticated sources. Additionally, enforce least-privilege container execution policies (such as OpenShift restricted security context constraints) and apply network policies to restrict outbound access from workbench pods, thereby containing the blast radius of any unauthorized execution.
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 | 8 | N/A | N/A |
| Attack Vector | Network | N/A | N/A |
| Attack Complexity | Low | N/A | N/A |
| Privileges Required | Low | N/A | N/A |
| User Interaction | Required | N/A | N/A |
| Scope | Unchanged | N/A | N/A |
| Confidentiality | High | N/A | N/A |
| Integrity Impact | High | N/A | N/A |
| Availability Impact | High | N/A | N/A |
Vector
Red Hat: CVSS:3.1/AV:N/AC:L/PR:L/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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