CVE-2023-43472 in MLFlow
Summary
by MITRE • 12/05/2023
An issue in MLFlow versions 2.8.1 and before allows a remote attacker to obtain sensitive information via a crafted request to REST API.
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Analysis
by VulDB Data Team • 12/23/2023
The vulnerability identified as CVE-2023-43472 represents a significant security weakness in MLFlow's REST API implementation that affects versions 2.8.1 and earlier. This issue stems from insufficient input validation and access control mechanisms within the MLFlow platform's web services, creating an avenue for remote attackers to exploit the system and extract sensitive information. The vulnerability specifically targets the application programming interface that governs how external systems interact with MLFlow's core functionalities, potentially exposing confidential data through improperly handled API requests.
The technical flaw manifests when the MLFlow REST API fails to adequately sanitize or validate incoming requests, allowing malicious actors to craft specific payloads that can bypass normal access controls. This weakness enables unauthorized information disclosure through carefully constructed API calls that exploit the platform's insufficient validation routines. The vulnerability can be categorized under CWE-200, which addresses information exposure, and represents a classic example of how inadequate input validation can lead to data leakage in web applications. Attackers can leverage this vulnerability to access sensitive metadata, model information, or other confidential data that should remain restricted to authorized users.
The operational impact of CVE-2023-43472 extends beyond simple information disclosure, as it can potentially enable more sophisticated attacks within the MLFlow ecosystem. Remote attackers who successfully exploit this vulnerability can gain insights into the underlying machine learning workflows, model configurations, and potentially even training data or experimental results. This information can be particularly valuable in environments where MLFlow is used for proprietary machine learning projects or in regulated industries where data confidentiality is paramount. The vulnerability can be exploited through the standard REST API endpoints, making it accessible to attackers without requiring physical access to the system or elevated privileges within the network.
Organizations utilizing MLFlow versions prior to 2.8.2 should implement immediate mitigations to protect against potential exploitation of this vulnerability. The most effective solution involves upgrading to MLFlow version 2.8.2 or later, which includes patches specifically designed to address the API validation issues. Additionally, network administrators should consider implementing API rate limiting and monitoring mechanisms to detect unusual request patterns that might indicate exploitation attempts. From an ATT&CK framework perspective, this vulnerability aligns with techniques involving information gathering and credential access, as it provides attackers with reconnaissance capabilities that can inform subsequent attack phases. Organizations should also review their existing access control policies and implement principle of least privilege configurations to minimize potential damage from any successful exploitation attempts.