CVE-2026-96775 in MLflow
Summary
by MITRE • 09/23/2026
MLflow's dspy flavor, versions >= 2.0, applies the MLFLOW_ALLOW_PICKLE_DESERIALIZATION=False security control only when the model_path ends in .pkl, 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 in MLflow's DSPy flavor affects versions greater than or equal to 2.0 and centers on an insufficient validation of file extensions when applying security controls related to pickle deserialization. MLflow is a widely used platform for managing the machine learning lifecycle, including experiment tracking, reproducibility, and deployment. A critical component of its security model involves preventing arbitrary code execution through maliciously crafted serialized objects. The Python pickle module allows for object serialization but poses significant risks because it can execute arbitrary code during deserialization if the input data is tampered with by an attacker. To mitigate this risk, MLflow provides a configuration option called MLFLOW_ALLOW_PICKLE_DESERIALIZATION which defaults to False in many secure configurations, intending to block such dangerous operations unless explicitly permitted by the administrator.
The technical flaw lies specifically within the logic that determines whether this security control is enforced based on the file extension of the model artifact being loaded. The implementation incorrectly restricts the application of the MLFLOW_ALLOW_PICKLE_DESERIALIZATION=False setting exclusively to files ending with the .pkl extension. This creates a bypass mechanism where attackers can circumvent the deserialization protection by saving their malicious payload in alternative formats that are still processed as pickle objects but do not carry the expected file suffix. For instance, if an attacker crafts a model artifact using a different extension or no extension at all while embedding serialized Python code within it, the system may proceed to deserialize the object without triggering the protective block designed for .pkl files. This oversight stems from relying on superficial filename inspection rather than content-based validation of the data structure itself.
From an operational perspective, this vulnerability allows a remote attacker who has access to upload or modify MLmodel artifacts within the MLflow tracking server to achieve Remote Code Execution (RCE). By submitting a crafted artifact that exploits the extension mismatch, the attacker can inject arbitrary Python commands into the deserialization process. When the system attempts to load and utilize this model for inference or evaluation, it inadvertently executes the injected code with the privileges of the user running the MLflow service. This could lead to complete compromise of the underlying infrastructure, including data exfiltration, lateral movement within the network, or disruption of machine learning services. The impact is particularly severe in environments where multiple users contribute models or where model artifacts are sourced from untrusted repositories, as it undermines the integrity and confidentiality guarantees provided by the platform's security controls.
This issue aligns with CWE-94 Improper Control of Generation of Code (Code Injection) and specifically relates to CWE-502 Deserialization of Untrusted Data. The failure to validate data content independently of its filename represents a classic example of relying on security through obscurity or superficial checks rather than robust input validation. In the context of the MITRE ATT&CK framework, this vulnerability facilitates techniques associated with Initial Access and Execution via remote services, particularly those involving artifact manipulation for code execution. Attackers can leverage such flaws to establish persistence or escalate privileges within cloud-native machine learning environments that rely heavily on MLflow for model management.
To mitigate this risk, organizations should immediately update MLflow to a patched version where the deserialization security controls are applied based on content type detection rather than file extension matching. Administrators must ensure that the MLFLOW_ALLOW_PICKLE_DESERIALIZATION environment variable remains set to False in all production environments and is not overridden by application logic unless absolutely necessary for trusted internal workflows. Additionally, implementing strict access controls over who can upload or modify model artifacts reduces the attack surface significantly. It is also advisable to adopt alternative serialization formats such as JSON or protobuf where possible, which do not carry inherent code execution risks during deserialization. Regular security audits of MLflow configurations and continuous monitoring for anomalous loading patterns can further enhance resilience against exploitation attempts targeting this class of vulnerabilities.