CVE-2025-65530 is a critical vulnerability identified in the malware de-obfuscation component of CloudLinux ai-bolit versions prior to 32.7.4. The root cause is an eval injection (CWE-95) within the malware scanning routines, where user-supplied input from scanned files is improperly evaluated as code. This flaw allows an attacker to craft malicious files that, when scanned by ai-bolit, trigger arbitrary code execution with root privileges. The vulnerability does not require authentication (PR:N) but does require user interaction (UI:R), meaning the attacker must get the target system to scan the malicious file. The CVSS v3.1 score of 8.8 reflects the high impact on confidentiality, integrity, and availability, as the attacker can overwrite arbitrary files as root, potentially leading to full system compromise. No public exploits have been reported yet, but the nature of eval injection vulnerabilities makes exploitation straightforward once a crafted file is introduced. The vulnerability affects systems that use ai-bolit for malware analysis or automated scanning, common in hosting environments and security operations. The absence of a patch link suggests that remediation involves upgrading to version 32.7.4 or later, which presumably addresses the eval injection flaw. Given the root-level impact, this vulnerability poses a significant threat to any organization relying on CloudLinux ai-bolit for security automation.

For European organizations, the impact of CVE-2025-65530 is substantial. Organizations running CloudLinux ai-bolit in their security infrastructure risk full system compromise due to arbitrary file overwrite as root. This can lead to data breaches, service disruption, and persistent backdoors. The vulnerability threatens confidentiality by allowing attackers to access sensitive data, integrity by enabling modification or destruction of critical files, and availability by potentially disabling systems or services. Hosting providers and managed security service providers using ai-bolit for malware scanning are particularly vulnerable, as exploitation could affect multiple clients. The requirement for user interaction means phishing or social engineering could be used to introduce malicious files. The lack of known exploits in the wild provides a window for proactive mitigation, but the high severity demands immediate attention to prevent potential targeted attacks, especially in sectors with high regulatory scrutiny such as finance, healthcare, and critical infrastructure in Europe.

1. Upgrade CloudLinux ai-bolit to version 32.7.4 or later immediately to apply the fix for the eval injection vulnerability. 2. Restrict access to malware scanning functionalities to trusted users and systems only, minimizing exposure to untrusted files. 3. Implement strict input validation and sanitization on files before scanning to reduce the risk of malicious payloads triggering code execution. 4. Employ sandboxing techniques for malware analysis to isolate scanning processes from critical system components and limit root-level access. 5. Monitor logs and system behavior for unusual file modifications or scanning activity indicative of exploitation attempts. 6. Educate users and administrators about the risk of scanning untrusted files and enforce policies to prevent scanning of files from unknown or unverified sources. 7. Consider deploying additional endpoint detection and response (EDR) tools to detect and respond to suspicious activity related to ai-bolit scanning processes. 8. Regularly review and update security configurations and conduct penetration testing to validate the effectiveness of mitigations.