Vulnerability Monitor

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CVE-2024-3099


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.


Security Impact Summary

This vulnerability carries a MEDIUM severity rating with a CVSS v3.1 score of 5.4, indicating it can be exploited remotely over the network with relatively low complexity without requiring user interaction requiring only low-level privileges . The vulnerability impacts limited integrity, and limited availability for affected systems. Impacting 1 product from lfprojects organizations running these solutions should prioritize assessment and patching.

Historical Context

Reported in 2024, this vulnerability emerged during an era marked by increased sophistication in supply chain attacks, cloud infrastructure vulnerabilities, and software-as-a-service (SaaS) security challenges. Security practices during this period emphasized zero-trust architectures, container security, and API protection.


Published

2024-06-06T19:15:59.393

Last Modified

2024-11-21T09:28:53.953

Status

Modified

Source

[email protected]

Severity

CVSSv3.1: 5.4 (MEDIUM)

Weaknesses
  • Type: Secondary
    CWE-475
  • Type: Primary
    NVD-CWE-Other

Affected Vendors & Products
Type Vendor Product Version/Range Vulnerable?
Application lfprojects mlflow - Yes

References

How SecUtils Interprets This CVE

SecUtils normalizes and enriches National Vulnerability Database (NVD) records by standardizing vendor and product identifiers, aggregating vulnerability metadata from both NVD and MITRE sources, and providing structured context for security teams. For lfprojects's affected products, we extract Common Platform Enumeration (CPE) data, Common Weakness Enumeration (CWE) classifications, CVSS severity metrics, and reference data to enable rapid vulnerability prioritization and asset correlation. This record contains no exploit code, proof-of-concept instructions, or attack methodologies—only defensive intelligence necessary for patch management, risk assessment, and security operations.