Mlflow
CVE-2024-27133
CRITICAL
Vulnerability Assessment
| Weakness |
CWE
OWASP Top 10:2025
A05:2025 - Injection
ASVS 5.0
V1 - Encoding and Sanitization Output encoding, context-aware escaping
MITRE ATT&CK
2 techniques across 2 tactics: Initial Access, Execution The technique list with ATT&CK links and the kill-chain view is part of the Basic plan and up. |
| Impact | CVSS score of 9.6 (CRITICAL). Attack characteristics: network attack vector, low complexity, no privileges required, user interaction required. Public proof-of-concept exploit code is available. |
| Exploit ScenarioDerived | A remote attacker could inject malicious scripts into web pages viewed by other users by sending crafted requests to the affected application. No authentication is required to exploit this vulnerability. |
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Alert meAnalysisAI
Insufficient sanitization in MLflow leads to XSS when running a recipe that uses an untrusted dataset. Rated critical severity (CVSS 9.6), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available.
RemediationAI
A vendor patch is available. Apply the latest security update as soon as possible. Sanitize all user input, use Content-Security-Policy headers, encode output contextually (HTML, JS, URL). Use frameworks with built-in XSS protection.
Affected Packages
Source: OSV (Open Source Vulnerabilities)
Affects your stack
Affects 1 package in pypi
Checking this CVE against your own stacks and servers is part of the Basic plan and up.
Technical ContextAI · show details
This vulnerability is classified as Cross-Site Scripting (XSS) (CWE-79), which allows attackers to inject malicious scripts into web pages viewed by other users. 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. Affected products include: Lfprojects Mlflow.
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- Exploit and PoC references, threat intelligence from 22 sources
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Severity by source
AV:N/AC:L/PR:N/UI:R/S:C/C:H/I:H/A:H
Primary rating from NVD · only source for this CVE.
CVSS VectorNVD
DescriptionNVD
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.
Blast Radius
ecosystem impact- 3 pypi packages depend on mlflow (3 direct, 0 indirect)
Ecosystem-wide dependent count for version 2.10.0.
Threat Intelligence Overview
NVD
CVE.org
EPSS
GitHub
How this decision was reached
The engine reads each signal below and applies a fixed list of rules from top to bottom. The first rule that matches sets the recommended action – like a statement, every line is shown.
What every action level means
SSVC AssessmentENISA
Priority Score
Attack Technique
Cross-Site Scripting occurs when an application accepts untrusted data and sends it to a web browser without proper validation or encoding.
Affected Products (CPE)NVD
Server-side request forgery in MLflow before 3.15.0 lets remote unauthenticated attackers coerce the server into making
A path traversal vulnerability exists in mlflow/mlflow version 2.15.1. Rated high severity (CVSS 7.5), this vulnerabilit
Absolute Path Traversal in GitHub repository mlflow/mlflow prior to 2.5.0. Rated critical severity (CVSS 10.0), this vul
MLflow's FastAPI job endpoints bypass basic-auth entirely, allowing network attackers to submit and execute jobs without
A malicious user could use this issue to get command execution on the vulnerable machine and get access to data & models
A malicious user could use this issue to access internal HTTP(s) servers and in the worst case (ie: aws instance) it cou
An attacker is able to arbitrarily create an account in MLflow bypassing any authentication requirment. Rated critical s
An attacker can overwrite any file on the server hosting MLflow without any authentication. Rated critical severity (CVS
Path Traversal: '\..\filename' in GitHub repository mlflow/mlflow prior to 2.3.1. Rated critical severity (CVSS 9.8), th
Path Traversal: '\..\filename' in GitHub repository mlflow/mlflow prior to 2.2.1. Rated critical severity (CVSS 9.8), th
Insufficient sanitization in MLflow leads to XSS when running an untrusted recipe. Rated critical severity (CVSS 9.6), t
mlflow/mlflow is vulnerable to Local File Inclusion (LFI) due to improper parsing of URIs, allowing attackers to bypass
Exploit-DB