CVE-2025-53134 is a vulnerability identified in Microsoft Windows 10 Version 1809, specifically in the Windows Ancillary Function Driver for WinSock. The flaw is a Time-of-check Time-of-use (TOCTOU) race condition (CWE-367) that arises from improper synchronization when multiple threads or processes concurrently access shared resources. This race condition allows an authorized local attacker—someone with limited privileges on the affected system—to manipulate the timing of resource checks and usage to escalate their privileges. The vulnerability affects Windows 10 build 17763.0, which corresponds to the 1809 release. The CVSS v3.1 base score is 7.0, indicating high severity, with an attack vector of local access (AV:L), high attack complexity (AC:H), requiring low privileges (PR:L), no user interaction (UI:N), and unchanged scope (S:U). The impact on confidentiality, integrity, and availability is high (C:H/I:H/A:H), meaning an attacker can fully compromise the system once exploited. No known exploits are currently reported in the wild, and no patches have been linked yet, but the vulnerability is publicly disclosed and assigned a CVE ID. The root cause is a race condition in the WinSock driver, which handles network socket operations, making it a critical component for network communications. Exploiting this flaw requires precise timing and local access, limiting remote exploitation but still posing a significant risk in environments where untrusted users have local access. The vulnerability's presence in an older Windows 10 version means that organizations still running this release without upgrades or mitigations remain vulnerable.

For European organizations, the impact of CVE-2025-53134 can be substantial, particularly in sectors where Windows 10 Version 1809 is still operational, such as manufacturing, healthcare, and government agencies with legacy systems. Successful exploitation allows attackers to elevate privileges locally, potentially leading to full system compromise, data breaches, and disruption of critical services. This can undermine confidentiality by exposing sensitive data, integrity by allowing unauthorized changes, and availability by enabling denial-of-service conditions or persistent malware installation. The high attack complexity and requirement for local access reduce the likelihood of widespread exploitation but do not eliminate risk in environments with many users or insufficient endpoint controls. Organizations with remote or shared workstation access, or those using thin clients and virtual desktop infrastructure, may face increased exposure. The lack of known exploits in the wild provides a window for proactive mitigation, but the absence of patches means risk remains until updates are available or systems are upgraded. The vulnerability also poses a compliance risk under European data protection regulations if exploited to access personal or sensitive data.

To mitigate CVE-2025-53134, European organizations should prioritize upgrading affected systems from Windows 10 Version 1809 to a supported and patched Windows release, as this is the most effective long-term solution. Until upgrades are feasible, organizations should implement strict local access controls, limiting user privileges and restricting access to systems running this version. Employ application whitelisting and endpoint detection and response (EDR) solutions to monitor for suspicious activity related to WinSock driver usage or unusual privilege escalation attempts. Network segmentation can reduce the risk of lateral movement if a local compromise occurs. Regularly audit and harden local accounts, disable unnecessary services, and enforce strong authentication policies. Additionally, organizations should stay alert for official patches or security advisories from Microsoft and apply them promptly once available. Security teams should also educate users about the risks of local privilege escalation and monitor logs for anomalies indicative of exploitation attempts. Finally, consider deploying host-based intrusion prevention systems (HIPS) that can detect race condition exploitation patterns.