CVE-2025-26086 is a high-severity vulnerability identified in the RSI Queue Management System version 3.0. The flaw is an unauthenticated blind SQL injection located in the TaskID parameter of the system's GET request handler. This vulnerability allows remote attackers to inject specially crafted SQL payloads that cause time delays in server responses. By measuring these delays, attackers can perform time-based inference attacks to iteratively extract sensitive information from the backend database without requiring any authentication or user interaction. The vulnerability is classified under CWE-89, which corresponds to SQL injection issues. The attack vector is network-based (AV:N), with low attack complexity (AC:L), no privileges required (PR:N), and no user interaction needed (UI:N). The scope is unchanged (S:U), and the impact is high on confidentiality (C:H), with no impact on integrity (I:N) or availability (A:N). Although no official patch links are currently available, the vulnerability has been published and enriched by CISA, indicating recognition by authoritative cybersecurity entities. No known exploits are reported in the wild yet, but the nature of the vulnerability suggests a significant risk of data leakage through automated or manual exploitation techniques. The lack of authentication and the ability to remotely exploit the flaw make it particularly dangerous, as attackers can stealthily extract sensitive database contents, potentially including user data, system configurations, or other confidential information stored within the queue management system's database.

For European organizations using the RSI Queue Management System v3.0, this vulnerability poses a critical risk to the confidentiality of sensitive data. Queue management systems often handle customer information, service requests, and operational data, which if exposed, could lead to privacy violations, regulatory non-compliance (e.g., GDPR breaches), and reputational damage. The unauthenticated nature of the exploit means that attackers do not need valid credentials or insider access, increasing the attack surface significantly. The time-based blind SQL injection technique can be automated, allowing attackers to extract large volumes of data over time without detection. This could facilitate further attacks such as identity theft, fraud, or targeted phishing campaigns against affected organizations. Additionally, the exposure of internal data could undermine trust in service providers and disrupt business operations indirectly through compliance investigations or loss of customer confidence. Given the high confidentiality impact and the absence of integrity or availability effects, the primary concern is data leakage rather than service disruption or data manipulation.

European organizations should prioritize the following specific mitigation steps: 1) Immediate implementation of input validation and parameterized queries or prepared statements in the RSI Queue Management System to prevent SQL injection. 2) Employ web application firewalls (WAFs) configured to detect and block time-based SQL injection patterns, especially targeting the TaskID parameter in GET requests. 3) Conduct thorough code reviews and security testing focused on injection vulnerabilities within the queue management system. 4) Monitor network traffic and application logs for unusual delays or repetitive requests indicative of time-based inference attacks. 5) Restrict external access to the queue management system where possible, limiting exposure to trusted networks or VPNs. 6) Engage with the vendor or development team to obtain patches or updates as soon as they become available and apply them promptly. 7) Implement database-level protections such as least privilege access controls and query execution time limits to reduce the impact of potential exploitation. 8) Educate IT and security teams about this specific vulnerability to enhance detection and response capabilities. These measures go beyond generic advice by focusing on the unique characteristics of this blind SQL injection vulnerability and the operational context of queue management systems.