CVE-2023-5245 : What You Need to Know

This article provides detailed insights into CVE-2023-5245, including its description, impact, technical details, affected systems, and mitigation strategies.

Understanding CVE-2023-5245

CVE-2023-5245 is a security vulnerability that can be exploited when using MLeap for loading a saved model (zip archive), potentially leading to path traversal, arbitrary file creation, and remote code execution.

What is CVE-2023-5245?

The vulnerability arises from the FileUtil.extract() function within MLeap, which fails to validate file paths in zip archives properly. When creating a TensorflowModel instance with an exported tensorflow model in the saved_model format, the apply() function invokes the vulnerable implementation of FileUtil.extract(). This flaw allows an attacker to create arbitrary files, leading to potential code execution.

The Impact of CVE-2023-5245

The CVSSv3.1 base score for CVE-2023-5245 is 7.5, indicating a high-severity vulnerability. With a high impact on confidentiality, integrity, and availability, this vulnerability poses a significant risk. The attack complexity is rated as high, with a low level of privileges required for exploitation.

Technical Details of CVE-2023-5245

The following technical details shed light on the vulnerability:

Vulnerability Description

The vulnerability stems from the improper limitation of a pathname to a restricted directory, also known as 'Path Traversal' (CWE-22). FileUtil.extract() does not properly validate file paths in zip archives, allowing for arbitrary file creation and potential code execution.

Affected Systems and Versions

The vulnerability affects versions of

ml.combust.mleap.mleap-tensorflow

ranging from 0.18.0 to less than 0.23.1. Users of these versions are potentially impacted by CVE-2023-5245.

Exploitation Mechanism

Attackers can exploit this vulnerability by crafting a malicious zip archive with manipulated file paths that, when processed by MLeap's TensorflowModel, trigger the vulnerable FileUtil.extract() function. This can result in arbitrary file creation and code execution.

Mitigation and Prevention

To safeguard against CVE-2023-5245 and similar vulnerabilities, the following measures can be taken:

Immediate Steps to Take

Update to a patched version: Ensure that you are using a version of

ml.combust.mleap.mleap-tensorflow

that includes the necessary security patches to address this vulnerability.
Implement input validation: Validate file paths and archive contents rigorously to prevent path traversal attacks.
Monitor for suspicious activity: Keep a close eye on file creation and execution activities within your systems to detect any potential exploits.

Long-Term Security Practices

Regular code reviews: Conduct thorough code reviews to identify and address potential vulnerabilities in your applications.
Security training: Provide security awareness training to developers to enhance their understanding of secure coding practices.
Use security tools: Employ security tools and scanners to continuously monitor and identify security flaws in your code.

Patching and Updates

Ensure that you stay updated with security advisories from JFROG and promptly apply patches or updates released to address vulnerabilities like CVE-2023-5245. Regularly check for security updates and apply them to mitigate potential risks.