CVE-2023-43667 in InLong
Summary
by MITRE • 10/25/2023
Improper Neutralization of Special Elements used in an SQL Command ('SQL Injection') vulnerability in Apache InLong.This issue affects Apache InLong: from 1.4.0 through 1.8.0, the attacker can create misleading or false records, making it harder to audit and trace malicious activities. Users are advised to upgrade to Apache InLong's 1.8.0 or cherry-pick [1] to solve it. [1] https://github.com/apache/inlong/pull/8628
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Analysis
by VulDB Data Team • 06/16/2025
The CVE-2023-43667 vulnerability represents a critical SQL injection flaw within Apache InLong, a comprehensive data integration platform designed for large-scale data processing and streaming. This vulnerability specifically manifests in the improper neutralization of special elements within SQL commands, creating a pathway for malicious actors to manipulate database operations through crafted input. The affected versions span from 1.4.0 through 1.8.0, indicating a substantial release window where systems could be compromised. The vulnerability's classification aligns with CWE-89, which defines SQL injection as a code injection technique that exploits vulnerabilities in applications that construct SQL queries from user inputs without proper sanitization.
The technical exploitation of this vulnerability enables attackers to inject malicious SQL commands into the application's database layer, potentially allowing them to execute unauthorized database operations. When an attacker successfully leverages this weakness, they can create misleading or false records within the system's data stores, fundamentally compromising the integrity of the data pipeline. This manipulation directly impacts the audit trail and traceability features that are essential for monitoring data flows and identifying security incidents. The vulnerability's impact extends beyond simple data corruption, as it undermines the fundamental trustworthiness of the data processing infrastructure.
From an operational perspective, the consequences of this vulnerability are severe for organizations relying on Apache InLong for their data integration needs. The ability to create false records means that security analysts and compliance officers may encounter misleading information during audits, making it significantly more difficult to detect and investigate actual security incidents. This degradation of audit capabilities creates a false sense of security while allowing malicious activities to remain undetected. The vulnerability directly impacts the system's ability to maintain data integrity, which is a core requirement for any data processing platform, particularly in regulated environments where audit trails must be reliable and tamper-proof.
The recommended remediation approach involves upgrading to Apache InLong version 1.8.0 or implementing the specific patch referenced in the advisory. This upgrade path addresses the root cause by implementing proper input sanitization and parameterized query construction techniques that prevent malicious SQL elements from being executed within the database context. The patch referenced in the advisory (PR #8628) likely implements proper escaping mechanisms and input validation that align with industry best practices for preventing SQL injection attacks. Organizations should also consider implementing additional security controls such as database query monitoring, intrusion detection systems, and regular security assessments to further protect their data integration infrastructure from similar vulnerabilities.
The vulnerability demonstrates the critical importance of input validation in database-driven applications and aligns with ATT&CK technique T1070.004, which covers the use of SQL injection for data manipulation. This attack pattern specifically targets the integrity of data systems, making it particularly dangerous in environments where data accuracy and auditability are paramount. The attack surface is further extended through potential lateral movement opportunities if attackers can manipulate the data flow to influence other system components that depend on the integrity of the processed data.