CVE-2014-7232 in Healthcare Discovery XR656
Summary
by MITRE
GE Healthcare Discovery XR656 and XR656 G2 has a password of (1) 2getin for the insite user, (2) 4$xray for the xruser user, and (3) #superxr for the root user, which has unspecified impact and attack vectors. NOTE: it is not clear whether these passwords are default, hardcoded, or dependent on another system or product that requires a fixed value.
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Analysis
by VulDB Data Team • 05/13/2018
The vulnerability identified in CVE-2014-7232 affects GE Healthcare Discovery XR656 and XR656 G2 medical imaging devices, representing a critical security weakness in healthcare IoT infrastructure. These devices are designed for radiological imaging and diagnostic purposes, making their security paramount for patient data protection and operational integrity. The vulnerability manifests through hardcoded and predictable default credentials that persist across multiple user accounts, creating an exploitable attack surface that could compromise the entire imaging system.
The technical flaw involves the presence of well-known default passwords for three distinct user accounts on the affected devices. The insite user account utilizes the password "2getin", the xruser account employs "4$xray", and the root user account has the password "#superxr". This hardcoded credential configuration represents a fundamental failure in secure authentication design principles and violates industry best practices for embedded system security. The vulnerability falls under CWE-798, which specifically addresses the use of hardcoded credentials, and CWE-259, addressing weak password requirements in authentication mechanisms.
The operational impact of this vulnerability extends beyond simple unauthorized access, as it provides attackers with elevated privileges that could enable complete system compromise. The presence of root-level credentials particularly exposes the device to severe consequences including data manipulation, system disruption, and potential patient safety risks. Attackers could exploit these credentials to access sensitive medical imaging data, modify system configurations, or disable critical diagnostic functions. The unspecified attack vectors suggest that this vulnerability could be exploited through various means including network-based attacks, physical access, or even social engineering approaches that leverage the predictable nature of these passwords.
From an adversarial perspective, this vulnerability aligns with ATT&CK technique T1078, which covers legitimate credentials and default passwords, and T1005, covering data from local system. The predictable nature of these passwords makes them particularly attractive targets for automated exploitation tools, potentially enabling widespread compromise of medical imaging systems across healthcare facilities. The vulnerability's impact is further amplified by the critical nature of medical imaging equipment, where system compromise could directly affect patient care delivery and data integrity. Organizations deploying these devices face significant regulatory compliance risks under HIPAA and other healthcare data protection requirements, as the presence of such hardcoded credentials creates audit trail violations and security posture weaknesses that could result in substantial penalties.
The ambiguity regarding whether these passwords are truly default, hardcoded, or dependent on other systems underscores the complexity of addressing such vulnerabilities in legacy medical equipment. Many healthcare organizations maintain these devices for extended periods beyond manufacturer support, making the discovery of such fundamental security flaws particularly problematic. Remediation efforts require careful consideration of device functionality, as changing passwords on medical equipment often involves complex procedures that must not disrupt critical patient care operations. The vulnerability demonstrates the critical importance of proper device lifecycle management and the need for comprehensive security assessments of medical IoT infrastructure.