CVE-2025-30056 is a critical code injection vulnerability identified in the CGM CLININET product developed by CGM. The vulnerability stems from the RunCommand function, which improperly handles input parameters by directly passing them to the system shell for execution without adequate validation or sanitization. This flaw corresponds to CWE-94, indicating improper control over code generation, allowing attackers to inject and execute arbitrary code remotely. The vulnerability has a CVSS 4.0 base score of 9.4, reflecting its critical severity. Notably, the attack vector is adjacent network (AV:A), meaning exploitation requires network access but not direct local access. The vulnerability does not require any privileges (PR:N), user interaction (UI:N), or authentication (AT:N), making it highly exploitable. The impact on confidentiality, integrity, and availability is rated high, indicating that successful exploitation could lead to full system compromise, data theft, or disruption of services. Although no known exploits are currently reported in the wild, the straightforward nature of the vulnerability and the lack of required authentication make it a significant threat. CGM CLININET is a clinical information system used in healthcare environments, which typically handle sensitive patient data and critical healthcare operations. The vulnerability could allow attackers to execute arbitrary commands on affected systems, potentially leading to unauthorized access to protected health information, disruption of clinical workflows, or deployment of ransomware or other malware. The absence of available patches at the time of publication increases the urgency for mitigation and risk management.

For European organizations, especially healthcare providers using CGM CLININET, the impact of this vulnerability is severe. Exploitation could lead to unauthorized access to sensitive patient data, violating GDPR requirements and resulting in significant legal and financial penalties. The ability to execute arbitrary code on clinical systems could disrupt healthcare delivery, endanger patient safety, and damage organizational reputation. Given the critical nature of healthcare infrastructure, attackers could leverage this vulnerability to deploy ransomware or conduct espionage, further amplifying the impact. The vulnerability's network-adjacent attack vector means that attackers could exploit it remotely from within hospital networks or connected partner networks, increasing the attack surface. The lack of authentication and user interaction requirements lowers the barrier for exploitation, making it a high-risk threat for European healthcare entities. Additionally, the potential for cascading effects exists if the compromised system is connected to broader hospital IT infrastructure, potentially affecting multiple systems and services.

1. Immediate network segmentation: Isolate CGM CLININET systems from general network access, restricting communication to only trusted and necessary devices to reduce exposure. 2. Implement strict input validation and filtering at network boundaries and application layers to detect and block suspicious command injection attempts. 3. Employ application-layer firewalls or intrusion prevention systems (IPS) configured with signatures or heuristics targeting command injection patterns specific to CGM CLININET. 4. Monitor system and application logs for unusual command execution or shell activity indicative of exploitation attempts. 5. Restrict permissions on systems running CGM CLININET to the minimum necessary, limiting the impact of potential code execution. 6. Engage with the vendor (CGM) for timely patch releases and apply updates as soon as they become available. 7. Conduct internal security assessments and penetration testing focused on this vulnerability to identify and remediate exposure. 8. Educate IT and security staff about the vulnerability and signs of exploitation to enhance detection and response capabilities. 9. Consider deploying endpoint detection and response (EDR) solutions on affected systems to detect anomalous behavior related to code injection.