Mlflow
CVE-2024-3099
MEDIUM
Vulnerability Assessment
| Weakness |
CWE
ASVS 5.0
V15 - Secure Coding and Architecture Undefined behavior for input
|
| Impact | CVSS score of 5.4 (MEDIUM). Attack characteristics: network attack vector, low complexity, low privileges required, no user interaction not required. Public proof-of-concept exploit code is available. |
| Exploit ScenarioDerived | This vulnerability can be exploited remotely over the network. Successful exploitation could compromise the medium-severity affected system. |
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Alert meAnalysisAI
A vulnerability in mlflow/mlflow version 2.11.1 allows attackers to create multiple models with the same name by exploiting URL encoding. Rated medium severity (CVSS 5.4), this vulnerability is remotely exploitable, low attack complexity. Public exploit code available and no vendor patch available.
RemediationAI
No vendor patch is available at time of analysis. Monitor vendor advisories for updates. Apply vendor patches when available. Implement network segmentation and monitoring as interim mitigations.
Affected Packages
Source: OSV (Open Source Vulnerabilities)
Affects your stack
Affects 1 package in pypi
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Technical ContextAI · show details
This vulnerability is classified under CWE-475. A vulnerability in mlflow/mlflow version 2.11.1 allows attackers to create multiple models with the same name by exploiting URL encoding. This flaw can lead to Denial of Service (DoS) as an authenticated user might not be able to use the intended model, as it will open a different model each time. Additionally, an attacker can exploit this vulnerability to perform data model poisoning by creating a model with the same name, potentially causing an authenticated user to become a victim by using the poisoned model. The issue stems from inadequate validation of model names, allowing for the creation of models with URL-encoded names that are treated as distinct from their URL-decoded counterparts. Affected products include: Lfprojects Mlflow. Version information: version 2.11.1.
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Severity by source
AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:L/A:L
Primary rating from NVD · only source for this CVE.
CVSS VectorNVD
DescriptionNVD
A vulnerability in mlflow/mlflow version 2.11.1 allows attackers to create multiple models with the same name by exploiting URL encoding. This flaw can lead to Denial of Service (DoS) as an authenticated user might not be able to use the intended model, as it will open a different model each time. Additionally, an attacker can exploit this vulnerability to perform data model poisoning by creating a model with the same name, potentially causing an authenticated user to become a victim by using the poisoned model. The issue stems from inadequate validation of model names, allowing for the creation of models with URL-encoded names that are treated as distinct from their URL-decoded counterparts.
Threat Intelligence Overview
NVD
CVE.org
EPSS
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
Denial of Service attacks render applications or systems unavailable by overwhelming resources or triggering failure conditions.
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 a recipe that uses an untrusted dataset. Rated critical se
Insufficient sanitization in MLflow leads to XSS when running an untrusted recipe. Rated critical severity (CVSS 9.6), t
GitHub PoC
Exploit-DB