AVID-2026-R1442

Description

Insufficient sanitization in MLflow leads to XSS when running a recipe that uses an untrusted dataset. (CVE-2024-27133)

Details

Insufficient sanitization in MLflow leads to XSS when running a recipe that uses an untrusted dataset. This issue leads to a client-side RCE when running the recipe in Jupyter Notebook. The vulnerability stems from lack of sanitization over dataset table fields.

Reason for inclusion in AVID: The CVE-2024-27133 entry describes an insufficient sanitization in MLflow causing cross-site scripting (XSS) and a potential client-side RCE when running a recipe with an untrusted dataset. MLflow is a widely used AI tooling/component in ML pipelines, experimentation, and deployment workflows. This vulnerability impacts software used to build/train/package/deploy AI systems, i.e., a software supply-chain component in the general-purpose AI stack, rather than hardware/firmware. The CVE provides explicit vulnerability behavior (XSS/RCE) and references, supporting evaluation as a security vulnerability in the AI software supply chain.

References

Affected or Relevant Artifacts

Developer: Unknown
Deployer: Unknown
Artifact Details:

Type	Name
System	Unknown System

Impact

AVID Taxonomy Categorization

Risk domains: Security
SEP subcategories: S0100: Software Vulnerability
Lifecycle stages: L06: Deployment

CVSS

Version	3.1
Vector String	CVSS:3.1/AV:N/AC:H/PR:N/UI:R/S:U/C:H/I:H/A:H
Base Score	7.5
Base Severity	🔴 High
Attack Vector	NETWORK
Attack Complexity	🔴 High
Privileges Required	NONE
User Interaction	REQUIRED
Scope	UNCHANGED
Confidentiality Impact	🔴 High
Integrity Impact	🔴 High
Availability Impact	🔴 High

CWE

ID	Description
CWE-79	CWE-79 Improper Neutralization of Input During Web Page Generation (‘Cross-site Scripting’)

Other information

Report Type: Advisory
Credits:
Date Reported: 2024-02-23
Version: 0.3.3
AVID Entry