Open-Source Machine Learning Systems Vulnerable to Security Threats

 Open-source machine learning (ML) systems are highly vulnerable to security threats, with 22 flaws identified across 15 projects. Notably, MLflow is particularly susceptible. These vulnerabilities expose systems to unauthorized access, data breaches, and operational compromise. For example, a flaw in Weave (CVE-2024-7340) allows low-privileged users to access sensitive files, including admin API keys. ZenML's access control issues enable attackers to escalate permissions and access confidential data. These findings emphasize the need for robust security protocols to safeguard open-source ML systems.

https://www.techradar.com/pro/Open-source-machine-learning-systems-are-highly-vulnerable-to-security-threats

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