Critical flaw in AI testing framework MLflow can lead to server and data compromise

MLflow, an open-source framework that’s used by many organizations to manage their machine-learning tests and record results, received a patch for a critical vulnerability that could allow attackers to extract sensitive information from servers such as SSH keys and AWS credentials. The attacks can be executed remotely without authentication because MLflow doesn’t implement authentication by default and an increasing number of MLflow deployments are directly exposed to the internet.

“Basically, every organization that uses this tool is at risk of losing their AI models, having an internal server compromised, and having their AWS account compromised,” Dan McInerney, a senior security engineer with cybersecurity startup Protect AI, told CSO. “It’s pretty brutal.”

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