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Threat actors are actively exploiting a critical unauthenticated SSRF flaw (CVE-2026-64849) in MLflow to harvest cloud credentials from internet-exposed instances. The vulnerability stems from the server following HTTP redirects without re-validating secondary addresses, bypassing existing URL allowlists that only check initial destinations. Immediate patching to version 3.15.0 or higher is essential for data engineering teams using this open-source platform to track experiments and deploy models.
Critical MLflow SSRF Vulnerability Exploited by Hackers in the Wild
Threat actors are actively exploiting a critical, unauthenticated server-side request forgery (SSRF) vulnerability in MLflow, the popular open-source platform widely used by data engineering and machine learning teams to track experiments, package code, and deploy models. Tracked as CVE-2026-64849 with a critical CVSS 3.1 score of 9.3, the flaw impacts all MLflow versions prior to […] The post Critical MLflow SSRF Vulnerability Exploited by Hackers in the Wild appeared first on Cyber Security News.
Threat actors are actively exploiting a critical, unauthenticated server-side request forgery (SSRF) vulnerability in MLflow, the popular open-source platform widely used by data engineering and machine learning teams to track experiments, package code, and deploy models. Tracked as CVE-2026-64849 with a critical CVSS 3.1 score of 9.3, the flaw impacts all MLflow versions prior to 3.15.0. Threat…
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