CVE-2026-96804 in MLflow
Summary
by MITRE • 09/23/2026
MLflow's statsmodel flavor, versions 2.1.0 to 3.14.0, omits the MLFLOW_ALLOW_PICKLE_DESERIALIZATION=False security control entirely in _load_model(), which allows a remote attacker to execute arbitrary code via a crafted MLmodel artifact.
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Analysis
by VulDB Data Team • 09/23/2026
The vulnerability identified within MLflow versions ranging from 2.1.0 through 3.14.0 represents a critical failure in the secure deserialization of machine learning model artifacts, specifically affecting the statsmodels flavor implementation. This flaw stems from an omission in the _load_model() function where the security control mechanism designed to prevent unsafe pickle loading is entirely bypassed or not enforced. In standard MLflow operations, the environment variable MLFLOW_ALLOW_PICKLE_DESERIALIZATION serves as a crucial safeguard, defaulting to False to ensure that only safe formats like PMML are loaded by default unless explicitly overridden for trusted environments. However, in this specific code path, the logic fails to check or respect this configuration setting, effectively leaving the deserialization process wide open to malicious input without requiring any explicit user consent or configuration change beyond uploading a model artifact.
From a technical perspective, the core issue lies in Python's pickle module, which is inherently unsafe for untrusted data because it allows arbitrary code execution during the unpickling process. When an attacker crafts a malicious MLmodel artifact containing serialized payloads that exploit specific classes within the statsmodels library or its dependencies, these payloads are executed immediately upon loading by the vulnerable _load_model() function. This behavior aligns with CWE-502, which describes Deserialization of Untrusted Data as a distinct weakness where applications fail to validate data before deserializing it, leading to potential remote code execution. The absence of the MLFLOW_ALLOW_PICKLE_DESERIALIZATION check means that even if an organization has configured their MLflow server to reject pickle files by default for security reasons, this specific flavor ignores those settings, creating a significant gap in the defense-in-depth strategy.
The operational impact of this vulnerability is severe, as it allows remote attackers who have access to upload model artifacts to achieve arbitrary code execution on the system running the MLflow tracking or serving server. This could lead to full compromise of the underlying infrastructure, including data exfiltration, lateral movement within the network, and disruption of machine learning services. The attack vector is classified under ATT&CK technique T1203, which covers Exploitation for Client Execution, although in this context it often translates to server-side execution depending on how MLflow is deployed. If MLflow is used as a central model registry or serving endpoint, an attacker could potentially compromise multiple downstream systems that consume models from the affected instance, amplifying the blast radius of the initial exploit.
Mitigation strategies must address both immediate remediation and long-term architectural changes. The most effective solution is to upgrade MLflow to version 3.14.1 or later, where this specific omission in the statsmodels flavor has been corrected to properly enforce the MLFLOW_ALLOW_PICKLE_DESERIALIZATION setting. For environments that cannot immediately patch due to dependency constraints, administrators should ensure that no untrusted users have permission to upload model artifacts and restrict access to the MLflow UI and API endpoints. Additionally, organizations should consider implementing network-level controls such as web application firewalls or strict ingress filtering to prevent malicious payloads from reaching the vulnerable service components until patches are applied. It is also advisable to audit existing logs for any signs of attempted deserialization attacks using known pickle exploit patterns associated with Python libraries commonly used in statistical modeling.