CVE-2026-72510 in TMS7
Summary
by MITRE • 09/30/2026
The "supplier_no" parameter used in the business allocation search feature is vulnerable to time-based blind SQL injection.
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Analysis
by VulDB Data Team • 09/30/2026
A critical security vulnerability has been identified within the supplier management module of a web application, specifically affecting the business allocation search functionality. The flaw resides in the handling of the supplier_no input parameter, which fails to properly sanitize user-supplied data before incorporating it into backend database queries. This lack of validation allows an unauthenticated or authenticated attacker to inject malicious SQL code directly into the query structure executed by the underlying relational database management system. Unlike standard injection attacks that rely on visible error messages or direct output manipulation, this specific instance manifests as a time-based blind vulnerability. In such scenarios, the application does not return data from the injected queries in its response body; instead, it reacts to the execution of those commands through observable delays in page load times or API response latency. This mechanism requires the attacker to construct payloads that trigger conditional sleep functions within the database engine, allowing them to infer information bit by bit based on whether a delay occurs.
The technical nature of this vulnerability aligns with Common Weakness Enumeration identifier CWE-89, which covers Improper Neutralization of Special Elements used in an SQL Command. The root cause is typically attributed to insufficient input validation and the absence of parameterized queries or prepared statements when constructing dynamic SQL strings. By concatenating user input directly into a query string without escaping special characters such as single quotes or semicolons, the application inadvertently grants the attacker control over the logic flow of the database command. The time-based blind aspect indicates that the developer may have implemented some basic filtering to prevent obvious data exfiltration via error messages, but failed to account for side-channel effects like execution time. This is a sophisticated attack vector often employed when direct output channels are restricted or disabled in production environments to mitigate other forms of injection attacks.
From an operational perspective, the impact of this vulnerability extends far beyond simple data leakage. An attacker can exploit this flaw to extract sensitive information stored within the database schema, including supplier details, financial records, internal identifiers, and potentially user credentials if they are accessible through joined tables or system views. The ability to perform time-based extraction means that even without immediate feedback, an adversary can systematically reconstruct entire databases by testing character values against conditional statements. This process is slower than error-based injection but remains highly effective for extracting structured data. Furthermore, depending on the privileges granted to the database user account used by the application, this vulnerability could potentially be escalated to achieve remote code execution or server compromise through features such as xp_cmdshell in Microsoft SQL Server or similar extensions in other database platforms that allow OS-level command execution via SQL commands.
This attack pattern is categorized under MITRE ATT&CK technique T1059.004, which refers to Command and Scripting Interpreter: SQL Commands. The exploitation involves the attacker crafting specific payloads designed to trigger conditional delays, such as using SLEEP() in MySQL or PostgreSQL, WAITFOR DELAY in Microsoft SQL Server, or pg_sleep() in Postgres. By analyzing the response time differences between requests containing true conditions and those with false conditions, an automated tool can deduce database contents character by character. This method is particularly dangerous because it bypasses many traditional web application firewalls that focus on blocking obvious injection keywords rather than monitoring for behavioral anomalies like latency spikes caused by sleep functions.
To mitigate this vulnerability, immediate remediation steps must be taken to ensure all user inputs are treated as data rather than executable code. The primary defense involves refactoring the affected search feature to use parameterized queries or prepared statements provided by the database driver and application framework. This approach ensures that the SQL engine distinguishes clearly between query logic and input values, rendering injection attempts harmless regardless of their content. Additionally, implementing strict input validation is crucial; this includes enforcing type checking for numeric fields like supplier_no, restricting allowed character sets to alphanumeric characters only, and applying length limits where appropriate. While these measures provide defense in depth, they should not replace the use of parameterized queries as the primary safeguard.
Beyond code-level fixes, organizations should implement comprehensive monitoring and detection strategies to identify potential exploitation attempts. Web application firewalls can be tuned to detect patterns associated with time-based blind injection, such as unusual latency spikes correlated with specific input parameters. Logging mechanisms should capture detailed information about database query execution times and anomalies in request payloads to facilitate rapid incident response. Regular security assessments, including both automated static analysis scanning for SQL construction flaws and manual penetration testing focused on business logic endpoints like supplier searches, are essential to maintain a robust security posture against evolving injection threats.