CVE-2013-4048 in SPSS Analytical Decision Management
Summary
by MITRE
Cross-site scripting (XSS) vulnerability in IBM SPSS Analytical Decision Management 6.1 before IF1, 6.2 before IF1, and 7.0 before FP1 IF6 allows remote authenticated users to inject arbitrary web script or HTML via vectors involving addition of script to a page.
Be aware that VulDB is the high quality source for vulnerability data.
Analysis
by VulDB Data Team • 02/28/2018
The vulnerability identified as CVE-2013-4048 represents a critical cross-site scripting flaw affecting IBM SPSS Analytical Decision Management versions 6.1 through 6.2 and 7.0 before specific fix packs. This vulnerability resides within the web application interface of the decision management platform, which is designed for business analytics and decision-making automation. The flaw enables authenticated attackers to inject malicious scripts into web pages that are subsequently executed by other users accessing the affected system. The vulnerability specifically manifests when users add script content to pages within the application's interface, creating a persistent XSS vector that can be exploited by adversaries with legitimate access credentials. This type of vulnerability falls under CWE-79 which categorizes cross-site scripting as a fundamental web application security weakness, and aligns with ATT&CK technique T1566.001 for initial access through malicious web content.
The technical implementation of this vulnerability stems from insufficient input validation and output encoding within the application's page composition features. When authenticated users add content to pages, the system fails to properly sanitize or encode script elements that might be embedded within the user-provided data. This inadequate sanitization process allows attackers to inject malicious javascript code that gets stored and executed when other users view the affected pages. The vulnerability requires authentication to exploit, meaning attackers must first obtain valid user credentials, but once achieved, they can persistently inject malicious code that affects all users who access the compromised pages. The attack vector specifically targets the application's content management capabilities, where users can modify page elements, making this particularly dangerous in collaborative environments where multiple users interact with shared analytical dashboards and decision models.
The operational impact of this vulnerability extends beyond simple script injection, as it can enable attackers to perform a wide range of malicious activities within the context of the affected application. Successful exploitation could allow attackers to steal session cookies, redirect users to malicious sites, modify content displayed to other users, or even execute arbitrary commands on the application server. The vulnerability poses significant risks to data integrity and confidentiality within business analytics environments, where decision management systems often contain sensitive business intelligence and strategic information. Attackers could potentially manipulate decision models, alter analytical results, or gain unauthorized access to business-critical data. The persistent nature of stored XSS vulnerabilities means that the malicious code remains active until manually removed, providing attackers with extended access windows and the ability to conduct prolonged surveillance or data exfiltration activities.
Organizations affected by this vulnerability should immediately implement comprehensive mitigation strategies to protect their analytical decision management systems. The primary recommendation involves applying the relevant IBM security fix packs and updates for the affected versions, specifically targeting the IF1, IF1, and FP1 IF6 releases mentioned in the vulnerability description. Network segmentation and access controls should be strengthened to limit the scope of potential exploitation, ensuring that only authorized personnel have access to the decision management interface. Input validation and output encoding mechanisms within the application should be enhanced to properly sanitize all user-provided content before it is stored or rendered. Regular security assessments and penetration testing should be conducted to identify additional vulnerabilities within the web application stack. Additionally, security awareness training for users should emphasize the importance of not clicking on suspicious links or content within decision management interfaces, and monitoring logs for unusual activity patterns that might indicate exploitation attempts. The vulnerability demonstrates the critical importance of proper web application security controls in enterprise analytics platforms where multiple users interact with shared decision-making systems.