EUVD-2024-1934

Comprehensive Technical Analysis of EUVD-2024-1934

1. Vulnerability Assessment and Severity Evaluation

The vulnerability identified as EUVD-2024-1934 (CVE-2024-0520) in mlflow/mlflow version 8.2.1 is a critical remote code execution (RCE) flaw. The vulnerability arises from improper neutralization of special elements used in an OS command ('Command Injection') within the mlflow.data.http_dataset_source.py module. This allows an attacker to control the file path fully by utilizing path traversal or absolute path techniques, leading to arbitrary file write and potential command execution.

Severity Evaluation:

• Base Score: 10.0 (Critical)
• Base Score Vector: CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:H

The CVSS score of 10.0 indicates the highest level of severity. The vulnerability can be exploited remotely (AV:N) with low complexity (AC:L), requires no privileges (PR:N), and no user interaction (UI:N). The impact on confidentiality, integrity, and availability is high (C:H/I:H/A:H), and the scope change (S:C) indicates that the vulnerability can affect components beyond the security scope managed by the security authority.

2. Potential Attack Vectors and Exploitation Methods

Attack Vectors:

• Remote Code Execution (RCE): An attacker can exploit this vulnerability by crafting a malicious URL or Content-Disposition header that includes path traversal or absolute path techniques. This allows the attacker to write arbitrary files to the system, potentially leading to command execution.
• Path Traversal: By manipulating the file path, an attacker can traverse directories and write files to sensitive locations, such as system directories or configuration files.

Exploitation Methods:

• Crafted URLs: An attacker can create a URL with a specially crafted path that, when processed by the vulnerable module, results in writing a file to an unintended location.
• Malicious Headers: An attacker can manipulate the Content-Disposition header to include path traversal sequences, leading to arbitrary file write.

3. Affected Systems and Software Versions

Affected Software:

• mlflow/mlflow version 8.2.1

Affected Systems:

• Any system running mlflow/mlflow version 8.2.1 that processes datasets from HTTP sources.

4. Recommended Mitigation Strategies

Immediate Mitigation:

• Upgrade to Version 2.9.0: The vulnerability is fixed in version 2.9.0. Organizations should upgrade to this version or later as soon as possible.
• Input Validation: Implement strict input validation and sanitization for URLs and headers to prevent path traversal and command injection.
• Least Privilege: Ensure that the mlflow service runs with the least privileges necessary to minimize the impact of a successful exploit.

Long-Term Mitigation:

• Regular Patching: Establish a regular patching and update schedule for all software components.
• Security Audits: Conduct regular security audits and code reviews to identify and mitigate similar vulnerabilities.
• Network Segmentation: Implement network segmentation to limit the attack surface and contain potential breaches.

5. Impact on European Cybersecurity Landscape

The vulnerability poses a significant risk to organizations using mlflow for machine learning workflows, particularly those in the European Union. The potential for remote code execution and data breaches can have severe implications for data privacy, compliance with regulations such as GDPR, and overall cybersecurity posture. Organizations must prioritize patching and implementing robust security measures to protect against such vulnerabilities.

6. Technical Details for Security Professionals

Vulnerability Details:

• Module: mlflow.data.http_dataset_source.py
• Issue: Improper neutralization of special elements used in an OS command ('Command Injection') when loading a dataset from a source URL with an HTTP scheme.
• Exploit: The filename extracted from the Content-Disposition header or the URL path is used to generate the final file path without proper sanitization, allowing path traversal and arbitrary file write.

References:

• NVD Entry: CVE-2024-0520
• GitHub Commit: Fix Commit
• GitHub Repository: mlflow/mlflow
• Huntr Bounty: Bounty Details

Assigner:

• @huntr_ai

EPSS Score:

• 2 (Indicates a moderate likelihood of exploitation in the wild)

ENISA IDs:

• Product: [{"id":"9bfdbf3e-7b5f-3df0-944d-011b03c8b87e","product":{"name":"mlflow/mlflow"},"product_version":"unspecified <2.9.0"}]
• Vendor: [{"id":"2602d4e2-c490-35da-adf0-16ca9e07d255","vendor":{"name":"mlflow"}}]

By addressing this vulnerability promptly and comprehensively, organizations can significantly reduce the risk of exploitation and maintain a robust cybersecurity posture.