CVE-2024-37059 is a high-severity vulnerability affecting versions of the MLflow platform starting from 0.5.0. This vulnerability allows for deserialization of untrusted data, which can lead to arbitrary code execution on an end user’s system when a maliciously uploaded PyTorch model is interacted with. The vulnerability has a CVSS score of 8.8, indicating a critical risk to users and organizations relying on the MLflow platform.
The exploitation of this vulnerability is particularly concerning due to its high impact on confidentiality, integrity, and availability. As attackers may leverage this vulnerability to execute arbitrary code, organizations must take immediate action to address this issue. Urgency for defenders is high, and they should prioritize patching to mitigate potential risks.
With no known exploits reported in the wild and the vulnerability not included in the Known Exploited Vulnerabilities (KEV) catalog, it remains crucial for organizations to remain vigilant. They should monitor for updates and ensure that their systems are protected against this vulnerability.
Given the circumstances, organizations should act to remediate this vulnerability as soon as possible, ideally within their priority patch cycle.
Vulnerability Details
The official CVE description outlines that deserialization of untrusted data can occur in MLflow versions 0.5.0 and newer, which may allow a maliciously uploaded PyTorch model to run arbitrary code on an end user's system. The vulnerability falls under the Common Weakness Enumeration (CWE) classification of CWE-502.
The CVSS score of 8.8 categorizes this vulnerability as high severity, highlighting the need for urgent remediation. The attack vector for this vulnerability is classified as network-based, with low complexity involved in its exploitation. No privileges are required to exploit the vulnerability, but user interaction is necessary.
The confidentiality, integrity, and availability impacts are all rated as high, indicating that exploitation could lead to significant breaches and disruptions.
Technical Analysis
The root cause of CVE-2024-37059 is related to improper handling of untrusted data during the deserialization process. The attack vector is network-based, allowing an attacker to exploit the vulnerability remotely. The attack complexity is categorized as low, meaning that a potential attacker can easily exploit the vulnerability without advanced technical skills.
No privileges are required for exploitation, which further increases the risk. User interaction is required, as the maliciously uploaded PyTorch model must be executed by the user. The impacts on confidentiality, integrity, and availability are rated high, indicating that an attacker could potentially gain unauthorized access to sensitive information or disrupt system operations.
Risk & Impact Analysis
Risk to organizations includes the potential for unauthorized access to sensitive data, disruption of services, and damage to reputation. The blast radius potential is significant due to the widespread use of the MLflow platform in machine learning applications. Organizations leveraging MLflow should evaluate their exposure to this vulnerability and implement necessary security measures.
With a CVSS score of 8.8 and an EPSS score indicating a relatively low probability of exploitation, organizations should still not underestimate this vulnerability. The urgency for remediation is categorized as high, given the potential impact on operations and data integrity.
Signal | Status |
|---|---|
Known Exploit | No |
Public PoC | No |
Actively Exploited | No |
Ransomware Use | No |
Affected Versions
The vulnerability affects all versions of the MLflow platform starting from version 0.5.0. Organizations should ensure they are running the latest patched version to mitigate the risk.
Mitigation & Remediation
Organizations should prioritize patching by updating to the latest version of MLflow that addresses this vulnerability. In the absence of a patch, consider implementing configuration changes to restrict the execution of untrusted code and monitor for any suspicious activity related to model uploads.
For additional insights into penetration testing and vulnerability management, organizations may refer to resources on penetration testing and best practices.
Detection Guidance
Organizations should monitor logs for indicators of unauthorized model uploads or unexpected execution of code. Behavioral anomalies in model interactions are also critical to detect potential exploitation of this vulnerability.
AppSecure Threat Intelligence Insight
The long-term significance of this vulnerability highlights the importance of securing machine learning frameworks against exploitation through untrusted data handling. As organizations adopt more AI and ML technologies, understanding and mitigating risks becomes paramount.
This vulnerability represents a trend towards increasing risks in AI-related technologies. Security teams should learn from such vulnerabilities to implement robust defensive measures and ensure security at every stage of AI model development.
For more insights on securing AI models, organizations can refer to our resources on AI security and cloud security assessments to better protect against future vulnerabilities.
Disclaimer: This content was generated using AI. While we strive for accuracy, please verify critical information with official sources.
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