CVE-2024-45189 in Mage AI
Summary
by MITRE • 08/23/2024
Mage AI allows remote users with the "Viewer" role to leak arbitrary files from the Mage server due to a path traversal in the "Git Content" request
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Analysis
by VulDB Data Team • 09/13/2024
The vulnerability CVE-2024-45189 represents a critical path traversal flaw within Mage AI that enables remote attackers with minimal privileges to access sensitive server files. This issue specifically affects the "Git Content" request functionality where improper input validation allows malicious users to manipulate file paths and retrieve arbitrary files from the underlying server filesystem. The vulnerability exists in the Mage AI platform's handling of file access requests, creating an unauthorized data exfiltration vector that could compromise sensitive information stored on the server.
The technical implementation of this vulnerability stems from inadequate sanitization of user-supplied input within the Git Content endpoint. When a remote user with the Viewer role submits a request to access Git content, the system fails to properly validate or sanitize the file path parameter, allowing attackers to craft malicious requests that traverse directory structures using techniques such as directory traversal sequences. This flaw aligns with CWE-22 Path Traversal vulnerabilities, which specifically address improper input validation that allows attackers to access files outside of intended directories. The vulnerability demonstrates how insufficient access controls and input validation can create dangerous pathways for information disclosure attacks.
The operational impact of this vulnerability extends beyond simple information leakage, as it provides attackers with potential access to sensitive configuration files, source code repositories, database credentials, and other confidential data stored on the Mage server. Remote attackers can exploit this vulnerability without requiring elevated privileges, making it particularly dangerous as it can be leveraged by threat actors with minimal initial access. The implications include potential data breaches, intellectual property theft, and the possibility of further exploitation through the retrieved sensitive information. This vulnerability directly maps to ATT&CK technique T1074 Data Staged, where adversaries collect data from systems for later exfiltration, and T1566 Impersonation, as attackers can exploit legitimate user roles to gain unauthorized access.
Organizations using Mage AI systems should immediately implement mitigation strategies including input validation, path normalization, and strict access controls for all file access endpoints. The recommended approach involves implementing proper parameter validation that rejects or sanitizes potentially dangerous path traversal sequences, enforcing strict file access controls, and implementing comprehensive logging of file access attempts. Additionally, organizations should conduct thorough security assessments of all file handling functions within their applications and consider implementing web application firewalls to detect and block malicious path traversal attempts. Regular security updates and patches should be applied to address this vulnerability promptly, as the risk of exploitation increases with the time between vulnerability disclosure and remediation.