https://vulnfeed.org/Every disclosure, as it lands. Critical-only feed available.enhttps://vulnfeed.org/cve/CVE-2026-118628/https://vulnfeed.org/cve/CVE-2026-118628/Wed, 04 Nov 2026 00:17:24 +0000MLflow get-artifacts Server-Side Request Forgery Information Disclosure Vulnerability. This vulnerability allows remote attackers to disclose sensitive information on affected installations of MLflow. Authentication is not required to exploit this vulnerability. The specific flaw exists within the get-artifacts function. The issue results from the lack of proper validation of a URI prior to accessing resources. An attacker can leverage this vulnerability to disclose information in the context of the server. Was ZDI-CAN-28192.https://vulnfeed.org/cve/CVE-2026-118623/https://vulnfeed.org/cve/CVE-2026-118623/Wed, 04 Nov 2026 00:17:23 +0000MLflow PickleEvaluationArtifact Deserialization of Untrusted Data Remote Code Execution Vulnerability. This vulnerability allows remote attackers to execute arbitrary code on affected installations of MLflow. User interaction is required to exploit this vulnerability in that the target must visit a malicious page or open a malicious file. The specific flaw exists within the parsing of pickle files. The issue results from the lack of proper validation of user-supplied data, which can result in deserialization of untrusted data. An attacker can leverage this vulnerability to execute code in the context of the current process. Was ZDI-CAN-29513.https://vulnfeed.org/cve/CVE-2026-118627/https://vulnfeed.org/cve/CVE-2026-118627/Wed, 04 Nov 2026 00:17:23 +0000MLflow SHAP load_explainer Deserialization of Untrusted Data Remote Code Execution Vulnerability. This vulnerability allows remote attackers to execute arbitrary code on affected installations of MLflow. User interaction is required to exploit this vulnerability in that the target must visit a malicious page or open a malicious file. The specific flaw exists within the loading of SHAP explainer models. The issue results from the lack of proper validation of user-supplied data, which can result in deserialization of untrusted data. An attacker can leverage this vulnerability to execute code in the context of the current user. Was ZDI-CAN-30301.https://vulnfeed.org/cve/CVE-2026-116692/https://vulnfeed.org/cve/CVE-2026-116692/Tue, 27 Oct 2026 20:03:49 +0000MLflow is an open source AI engineering platform for agents, large language models, and machine learning models. From 3.12.0rc0 until 3.17.0, third-party scorer deserialization in mlflow/genai/scorers/base.py accepts request-selected descendants of THIRD_PARTY_SCORER_ALLOWED_MODULES rather than requiring an exact allowed module. A caller can use MLflow artifact endpoints to place a Python module beneath a writable allowed package and submit its module and class through /ajax-api/3.0/mlflow/scorer/invoke. Scorer.model_validate_json() imports the module and constructs the class before trace processing. Authentication-disabled deployments permit this code execution without credentials, while basic-auth deployments require a principal capable of creating an experiment and run and uploading artifacts. In either configuration, the allowed scorer package must be writable by the server process. This issue is fixed in version 3.17.0.https://vulnfeed.org/cve/CVE-2026-64849/https://vulnfeed.org/cve/CVE-2026-64849/Wed, 19 Aug 2026 00:00:00 +0000MLflow contains a server-side request forgery vulnerability that can allow attackers to reach internal or cloud metadata services and receive response_status and response_body.