CVE-2023-31101 in InLong
Summary
by MITRE • 05/22/2023
Insecure Default Initialization of Resource Vulnerability in Apache Software Foundation Apache InLong.This issue affects Apache InLong: from 1.5.0 through 1.6.0. Users registered in InLong who joined later can see deleted users' data. Users are advised to upgrade to Apache InLong's 1.7.0 or cherry-pick https://github.com/apache/inlong/pull/7836 https://github.com/apache/inlong/pull/7836 to solve it.
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Analysis
by VulDB Data Team • 06/15/2023
The vulnerability identified as CVE-2023-31101 represents a critical insecure default initialization of resource flaw within Apache InLong, a data integration platform developed by the Apache Software Foundation. This security weakness manifests in the form of improper access control mechanisms that fail to properly isolate user data within the system. The vulnerability specifically impacts versions of Apache InLong ranging from 1.5.0 through 1.6.0, creating a persistent security gap where newly registered users can inadvertently access data that belongs to previously deleted users. This represents a fundamental breakdown in the system's data isolation principles and user privilege management, creating a scenario where sensitive information can be exposed to unauthorized individuals.
The technical flaw underlying this vulnerability stems from inadequate initialization procedures for resource access controls during user session establishment. When users register in the system, the default configuration fails to properly enforce data access boundaries, particularly when dealing with user lifecycle management. The root cause lies in how the system handles the transition of user permissions and data access rights when users are deleted from the system. This issue demonstrates poor implementation of the principle of least privilege, where deleted user accounts should have their access rights completely revoked and their associated data should be properly isolated from subsequent user sessions. The vulnerability operates at the application level and specifically affects the user management and data access control components of Apache InLong's architecture.
The operational impact of this vulnerability extends beyond simple data exposure to encompass potential data integrity and confidentiality breaches that could compromise the entire data integration ecosystem. When users can access deleted user data, it creates a significant risk of information leakage that could include sensitive business data, personal information, or proprietary datasets that were intended to be completely removed from access. This vulnerability directly violates security standards such as those outlined in CWE-284, which addresses improper access control, and aligns with ATT&CK techniques related to privilege escalation and data access abuse. The consequences can be particularly severe in enterprise environments where Apache InLong is used for processing sensitive data flows, as it allows for unauthorized data reconnaissance and potential misuse of information that should have been permanently removed.
Organizations utilizing affected versions of Apache InLong should immediately implement the recommended mitigation strategies to address this vulnerability. The primary solution involves upgrading to Apache InLong version 1.7.0 or applying the specific patches referenced through the pull requests mentioned in the advisory. These patches specifically address the resource initialization issues by implementing proper access control boundaries and ensuring that deleted user data is completely isolated from subsequent user sessions. Security teams should also conduct comprehensive audits of their InLong deployments to verify that no unauthorized access has occurred and consider implementing additional monitoring controls to detect potential exploitation attempts. The vulnerability serves as a reminder of the critical importance of proper resource initialization and access control implementation in distributed data processing systems, particularly those handling sensitive enterprise data flows.