CVE-2022-42806 is a high-severity vulnerability affecting Apple macOS, as well as iOS 16.1 and iPadOS 16, stemming from a race condition in the kernel. A race condition occurs when multiple threads or processes access shared resources concurrently without proper synchronization, leading to unpredictable behavior. In this case, the flaw allows an application to execute arbitrary code with kernel privileges by exploiting the timing window where locking mechanisms were insufficient. Kernel privileges represent the highest level of access on the system, enabling full control over the operating system, including the ability to bypass security controls, manipulate system processes, and access sensitive data. The vulnerability was addressed by Apple through improved locking mechanisms that prevent the race condition from being exploited. The CVSS 3.1 base score is 7.0, indicating a high severity level. The vector string (AV:L/AC:H/PR:N/UI:R/S:U/C:H/I:H/A:H) indicates that the attack requires local access (AV:L), high attack complexity (AC:H), no privileges required (PR:N), and user interaction (UI:R). The scope is unchanged (S:U), and the impact is high on confidentiality, integrity, and availability (C:H/I:H/A:H). No known exploits are currently reported in the wild. This vulnerability is classified under CWE-362 (Race Condition), which is a common weakness related to improper synchronization. Since the affected versions are unspecified but include macOS Ventura 13 and iOS/iPadOS 16, systems not updated to these patched versions remain vulnerable. Given the kernel-level access possible, exploitation could lead to full system compromise, persistent malware installation, and data exfiltration.

For European organizations, this vulnerability poses a significant risk, especially for enterprises and government entities relying on Apple macOS devices. The ability for an unprivileged local application to escalate privileges to kernel level could enable attackers to bypass endpoint security, install persistent backdoors, and access confidential information. This is particularly critical for sectors handling sensitive data such as finance, healthcare, and critical infrastructure. The requirement for local access and user interaction somewhat limits remote exploitation but does not eliminate risk, as phishing or social engineering could induce users to run malicious applications. The high impact on confidentiality, integrity, and availability means that successful exploitation could disrupt business operations, lead to data breaches, and compromise system integrity. Additionally, organizations with Bring Your Own Device (BYOD) policies or remote workforces using macOS devices may face increased exposure. The absence of known exploits in the wild currently reduces immediate threat but does not preclude targeted attacks or future exploit development.

1. Immediate deployment of Apple’s security updates: Organizations should prioritize updating all macOS, iOS, and iPadOS devices to versions that include the patch for CVE-2022-42806 (macOS Ventura 13, iOS 16.1, iPadOS 16). 2. Restrict local application installation: Implement application whitelisting and restrict installation of untrusted or unsigned applications to reduce the risk of malicious apps exploiting this vulnerability. 3. Enhance endpoint detection and response (EDR): Deploy EDR solutions capable of monitoring for unusual kernel-level activity or privilege escalation attempts on macOS devices. 4. User awareness training: Educate users on the risks of running untrusted applications and the importance of avoiding suspicious links or downloads that could lead to local exploitation. 5. Limit physical and local access: Enforce strict physical security controls and limit local user accounts with administrative privileges to reduce the attack surface. 6. Monitor for indicators of compromise (IoCs): Although no known exploits exist, organizations should monitor for anomalous behavior consistent with kernel-level compromise. 7. Network segmentation: Isolate macOS devices handling sensitive data to limit lateral movement in case of compromise. These steps go beyond generic patching by focusing on reducing the likelihood of local exploitation and improving detection capabilities.