CVE-2013-6739 in SPSS Modeler
Summary
by MITRE
IBM SPSS Modeler before 16 on UNIX allows remote authenticated users to bypass intended access restrictions via an SSO token. IBM X-Force ID: 89855.
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Analysis
by VulDB Data Team • 03/07/2023
The vulnerability identified as CVE-2013-6739 affects IBM SPSS Modeler version 16 and earlier running on UNIX operating systems. This security flaw represents a significant authorization bypass issue that allows remote authenticated users to circumvent intended access controls within the application. The vulnerability specifically impacts the Single Sign-On token implementation mechanism, which is designed to streamline user authentication while maintaining proper access boundaries. Attackers exploiting this weakness can leverage their authenticated session to gain unauthorized access to resources and functionalities they should not be permitted to access.
The technical nature of this vulnerability stems from improper validation of SSO tokens within the IBM SPSS Modeler authentication framework. When users authenticate to the system, the application generates and validates SSO tokens to maintain session integrity and access control. However, the flaw in the implementation allows malicious actors with valid credentials to manipulate or bypass the token validation process, effectively elevating their privileges or gaining access to restricted features. This represents a classic authorization bypass vulnerability that falls under the CWE-285 category of improper authorization controls. The vulnerability exists at the application layer where authentication tokens are processed, potentially allowing attackers to access sensitive data, modify system configurations, or execute unauthorized operations within the SPSS Modeler environment.
The operational impact of this vulnerability extends beyond simple unauthorized access, as it can enable attackers to compromise the integrity and confidentiality of analytical workloads processed through SPSS Modeler. Organizations using this software for statistical analysis, data mining, and predictive modeling may face significant risks including exposure of proprietary business intelligence, manipulation of analytical results, and potential data breaches. The remote nature of the exploit means attackers do not require physical access to the system or local network presence, making the vulnerability particularly dangerous in enterprise environments where SPSS Modeler might be deployed across multiple locations. This weakness can be leveraged as an initial foothold for further attacks within the network, potentially enabling lateral movement and privilege escalation through the broader attack chain defined in the MITRE ATT&CK framework.
Organizations should implement immediate mitigations including applying the vendor-provided security patches and updates for IBM SPSS Modeler 16 and later versions. The vulnerability can be addressed through proper authentication token validation mechanisms and ensuring that access controls are properly enforced at every interaction point within the application. Network segmentation strategies should be implemented to limit access to SPSS Modeler installations, and monitoring systems should be enhanced to detect unusual authentication patterns or access attempts. Additionally, organizations should conduct comprehensive security assessments of their SPSS Modeler deployments to identify any other potential authorization bypass vulnerabilities. Regular security updates and vulnerability management processes should be strengthened to prevent similar issues from arising in the future, particularly given the widespread use of statistical analysis tools in enterprise environments where data protection and access control are paramount considerations.