Mlflow
CVE-2024-3099
MEDIUM
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
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:L/A:L
Lifecycle Timeline
1Blast Radius
ecosystem impact- 4 pypi packages depend on mlflow (4 direct, 0 indirect)
Ecosystem-wide dependent count for version 2.11.3.
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.
AnalysisAI
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.
Technical ContextAI
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.
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.
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
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
mlflow/mlflow is vulnerable to Local File Inclusion (LFI) due to improper parsing of URIs, allowing attackers to bypass
A vulnerability in mlflow/mlflow version 8.2.1 allows for remote code execution due to improper neutralization of specia
Same weakness CWE-475 – Undefined Behavior for Input to API
View allSame technique Denial Of Service
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External POC / Exploit Code
Leaving vuln.today