Security Flaws Found in Major Open-Source ML Frameworks

Title: Critical Security Flaws Discovered in Popular Open-Source Machine Learning Tools

Cybersecurity researchers have recently unveiled significant security vulnerabilities affecting widely-used open-source machine learning (ML) frameworks, including MLflow, H2O, PyTorch, and MLeap. These flaws could potentially enable remote code execution (RCE), posing a critical threat to organizations that rely on these tools. The vulnerabilities, identified by JFrog, are part of a larger set of 22 security issues disclosed last month, emphasizing the urgent need for enhanced security measures in the ML domain.

Overview of Vulnerabilities in Machine Learning Tools

Unlike previously reported server-side flaws, the newly identified vulnerabilities allow exploitation of ML clients. These vulnerabilities reside in libraries that manage safe model formats, such as Safetensors, which are commonly used in machine learning applications. As JFrog notes, "Hijacking an ML client in an organization can allow attackers to perform extensive lateral movement within the organization." This can lead to unauthorized access to sensitive ML services, including ML Model Registries and MLOps Pipelines.

Detailed List of Security Vulnerabilities

The following vulnerabilities have been identified:

• CVE-2024-27132 (CVSS score: 7.2): An insufficient sanitization issue in MLflow that enables cross-site scripting (XSS) attacks when executing untrusted recipes in Jupyter Notebooks, potentially allowing client-side remote code execution (RCE).

• CVE-2024-6960 (CVSS score: 7.5): An unsafe deserialization flaw in H2O when importing untrusted ML models, which could lead to remote code execution (RCE).

• Path Traversal Issue in PyTorch: A vulnerability in PyTorch’s TorchScript feature can result in denial-of-service (DoS) or code execution through arbitrary file overwrites, impacting critical system files (No CVE identifier assigned).

• CVE-2023-5245 (CVSS score: 7.5): A path traversal vulnerability in MLeap when loading zipped models, which can lead to a Zip Slip exploit, resulting in arbitrary file overwrites and possible code execution.

Security Recommendations for Organizations

JFrog strongly advises against blindly loading ML models, even those from supposedly safe sources like Safetensors. Shachar Menashe, JFrog’s VP of Security Research, emphasized the importance of understanding the models in use: "To safeguard against these threats, it’s essential to know which models you’re using and never load untrusted ML models, even from a ‘safe’ ML repository." This caution is vital, as loading untrusted models can result in remote code execution, potentially causing widespread harm within an organization.

Conclusion

As the use of AI and machine learning continues to expand, so does the risk associated with these technologies. Organizations must remain vigilant and proactive in securing their ML environments. Implementing stringent security measures and educating teams about the risks of untrusted models can help mitigate potential threats.

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