CPE Details
LF Projects MLflow 2.18.0 Release Candidate 0
2.18.0
2025-01-22
13h57 +00:00
13h57 +00:00
2025-01-22
13h57 +00:00
13h57 +00:00
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CPE Name: cpe:2.3:a:lfprojects:mlflow:2.18.0:rc0:*:*:*:*:*:*
Informations
Vendor
lfprojectsProduct
mlflowVersion
2.18.0Update
rc0Related CVE
| CVE ID | Published | Description | Score | Severity |
|---|---|---|---|---|
| In mlflow/mlflow version 2.18, an admin is able to create a new user account without setting a password. This vulnerability could lead to security risks, as accounts without passwords may be susceptible to unauthorized access. Additionally, this issue violates best practices for secure user account management. The issue is fixed in version 2.19.0. | 5.5 |
Medium |
||
| Remote Code Execution can occur in versions of the MLflow platform running version 1.11.0 or newer, enabling a maliciously crafted MLproject to execute arbitrary code on an end user’s system when run. | 8.8 |
High |
||
| Deserialization of untrusted data can occur in versions of the MLflow platform running version 1.27.0 or newer, enabling a maliciously crafted Recipe to execute arbitrary code on an end user’s system when run. | 8.8 |
High |
||
| Deserialization of untrusted data can occur in versions of the MLflow platform running version 0.5.0 or newer, enabling a maliciously uploaded PyTorch model to run arbitrary code on an end user’s system when interacted with. | 8.8 |
High |
||
| Deserialization of untrusted data can occur in versions of the MLflow platform running version 2.5.0 or newer, enabling a maliciously uploaded Langchain AgentExecutor model to run arbitrary code on an end user’s system when interacted with. | 8.8 |
High |
||
| Deserialization of untrusted data can occur in versions of the MLflow platform running version 2.0.0rc0 or newer, enabling a maliciously uploaded Tensorflow model to run arbitrary code on an end user’s system when interacted with. | 8.8 |
High |
||
| Deserialization of untrusted data can occur in versions of the MLflow platform running version 1.23.0 or newer, enabling a maliciously uploaded LightGBM scikit-learn model to run arbitrary code on an end user’s system when interacted with. | 8.8 |
High |
||
| Deserialization of untrusted data can occur in versions of the MLflow platform running version 1.24.0 or newer, enabling a maliciously uploaded pmdarima model to run arbitrary code on an end user’s system when interacted with. | 8.8 |
High |
||
| Deserialization of untrusted data can occur in versions of the MLflow platform running version 0.9.0 or newer, enabling a maliciously uploaded PyFunc model to run arbitrary code on an end user’s system when interacted with. | 8.8 |
High |
||
| Deserialization of untrusted data can occur in versions of the MLflow platform running version 1.1.0 or newer, enabling a maliciously uploaded scikit-learn model to run arbitrary code on an end user’s system when interacted with. | 8.8 |
High |
||
| Deserialization of untrusted data can occur in versions of the MLflow platform running version 1.1.0 or newer, enabling a maliciously uploaded scikit-learn model to run arbitrary code on an end user’s system when interacted with. | 8.8 |
High |
||
| An attacker is able to arbitrarily create an account in MLflow bypassing any authentication requirment. | 9.8 |
Critical |
The information presented on CVE Find originates from several carefully selected reference sources. CVE data is provided by MITRE Corporation and the National Vulnerability Database (NVD). The Known Exploited Vulnerabilities (KEV) catalog is sourced from the Cybersecurity and Infrastructure Security Agency (CISA), while EPSS scores come from FIRST.org. Additionally, data regarding software weaknesses (CWE) and common attack patterns (CAPEC) is maintained by MITRE Corporation, and information on hardware and software configurations (CPE) is provided by the NVD.