Researchers Identify Over 20 Supply Chain Vulnerabilities in MLOps Platforms
Experts in cybersecurity are alerting us to the dangers posed by supply chain security in machine learning (ML) software after finding over 20 flaws that might be used to compromise MLOps platforms.
These weaknesses, which are categorized as inherent and implementation-based vulnerabilities, could lead to dangerous outcomes including loading malicious datasets or arbitrary code execution.
With a model registry serving as a repository for version-trained ML models, MLOps platforms enable the creation and execution of ML model pipelines. Subsequently, these models can be integrated into an application or made available for other clients to query through an API read more about Researchers Identify Over 20 Supply Chain Vulnerabilities in MLOps Platforms.
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