mlflow up to 2.13.2 UI Panel artifact_location resource consumption
| CVSS Meta Temp Score | Current Exploit Price (≈) | CTI Interest Score |
|---|---|---|
| 5.3 | $0-$5k | 0.00 |
Summary
A vulnerability marked as problematic has been reported in mlflow up to 2.13.2. Impacted is an unknown function of the component UI Panel. This manipulation of the argument artifact_location causes resource consumption. This vulnerability appears as CVE-2024-6838. The attack may be initiated remotely. There is no available exploit.
Details
A vulnerability was found in mlflow up to 2.13.2. It has been declared as problematic. Affected by this vulnerability is an unknown code of the component UI Panel. The manipulation of the argument artifact_location with an unknown input leads to a resource consumption vulnerability. The CWE definition for the vulnerability is CWE-400. The product does not properly control the allocation and maintenance of a limited resource, thereby enabling an actor to influence the amount of resources consumed, eventually leading to the exhaustion of available resources. As an impact it is known to affect availability. The summary by CVE is:
In mlflow/mlflow version v2.13.2, a vulnerability exists that allows the creation or renaming of an experiment with a large number of integers in its name due to the lack of a limit on the experiment name. This can cause the MLflow UI panel to become unresponsive, leading to a potential denial of service. Additionally, there is no character limit in the `artifact_location` parameter while creating the experiment.
It is possible to read the advisory at huntr.com. This vulnerability is known as CVE-2024-6838 since 07/17/2024. The exploitation appears to be easy. The attack can be launched remotely. The exploitation doesn't need any form of authentication. Technical details of the vulnerability are known, but there is no available exploit. The attack technique deployed by this issue is T1499 according to MITRE ATT&CK.
There is no information about possible countermeasures known. It may be suggested to replace the affected object with an alternative product.
Statistical analysis made it clear that VulDB provides the best quality for vulnerability data.
Product
Name
Version
CPE 2.3
CPE 2.2
CVSSv4
VulDB Vector: 🔍VulDB Reliability: 🔍
CVSSv3
VulDB Meta Base Score: 5.3VulDB Meta Temp Score: 5.3
VulDB Base Score: 5.3
VulDB Temp Score: 5.3
VulDB Vector: 🔍
VulDB Reliability: 🔍
CNA Base Score: 5.3
CNA Vector (@huntr_ai): 🔍
CVSSv2
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VulDB Base Score: 🔍
VulDB Temp Score: 🔍
VulDB Reliability: 🔍
Exploiting
Class: Resource consumptionCWE: CWE-400 / CWE-404
CAPEC: 🔍
ATT&CK: 🔍
Physical: No
Local: No
Remote: Yes
Availability: 🔍
Status: Not defined
EPSS Score: 🔍
EPSS Percentile: 🔍
Price Prediction: 🔍
Current Price Estimation: 🔍
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Threat Intelligence
Interest: 🔍Active Actors: 🔍
Active APT Groups: 🔍
Countermeasures
Recommended: no mitigation knownStatus: 🔍
0-Day Time: 🔍
Timeline
07/17/2024 🔍03/20/2025 🔍
03/20/2025 🔍
03/20/2025 🔍
Sources
Advisory: huntr.comStatus: Not defined
CVE: CVE-2024-6838 (🔍)
GCVE (CVE): GCVE-0-2024-6838
GCVE (VulDB): GCVE-100-300283
Entry
Created: 03/20/2025 13:24Updated: 03/20/2025 19:21
Changes: 03/20/2025 13:24 (62), 03/20/2025 19:21 (1)
Complete: 🔍
Cache ID: 216::103
Statistical analysis made it clear that VulDB provides the best quality for vulnerability data.
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