CVE-2025-62116 identifies a Missing Authorization vulnerability (CWE-862) in the Quadlayers AI Copilot product, affecting versions up to 1.4.7. This vulnerability arises from improperly configured access control mechanisms that fail to enforce authorization checks on certain operations or API endpoints. As a result, an unauthenticated attacker can perform actions that should be restricted, leading to unauthorized integrity modifications within the system. The vulnerability does not impact confidentiality or availability, meaning sensitive data exposure or service disruption is not directly caused by this flaw. The CVSS 3.1 base score of 5.3 reflects a medium severity, with the vector indicating network attack vector (AV:N), low attack complexity (AC:L), no privileges required (PR:N), no user interaction (UI:N), unchanged scope (S:U), no confidentiality impact (C:N), integrity impact (I:L), and no availability impact (A:N). No known exploits have been reported in the wild, and no official patches have been released at the time of publication. The vulnerability was reserved in early October 2025 and published at the end of December 2025. The issue is critical for organizations relying on AI Copilot for automation or decision support, as unauthorized integrity changes could lead to incorrect AI outputs or system misconfigurations. The root cause is a failure to properly validate user permissions before allowing certain operations, a common access control weakness. Detection may require detailed audit logs and behavioral analysis to identify unauthorized actions. Remediation will require vendor patches or configuration changes to enforce strict authorization checks.

For European organizations, the primary impact of CVE-2025-62116 lies in the potential unauthorized modification of AI Copilot configurations or data inputs, which could degrade the reliability and trustworthiness of AI-driven processes. This may affect sectors relying heavily on AI automation such as finance, healthcare, manufacturing, and public services. While confidentiality and availability remain intact, integrity compromises can lead to erroneous AI recommendations, flawed decision-making, or operational disruptions indirectly caused by corrupted AI outputs. Given the AI Copilot's role in augmenting workflows, unauthorized changes could propagate errors at scale. Organizations with regulatory obligations around data integrity and auditability (e.g., GDPR compliance) may face compliance risks if unauthorized modifications go undetected. The lack of required authentication and user interaction lowers the barrier for exploitation, increasing risk exposure. However, the absence of known exploits and patches currently reduces immediate threat but calls for proactive risk management. The impact is heightened in environments where AI Copilot is integrated into critical infrastructure or sensitive business processes.

1. Conduct a thorough access control review of AI Copilot deployments, ensuring all API endpoints and management interfaces enforce strict authorization checks. 2. Implement network segmentation to isolate AI Copilot systems from untrusted networks, limiting exposure to potential attackers. 3. Enable detailed logging and monitoring of AI Copilot activities, focusing on detecting unauthorized or anomalous operations. 4. Restrict administrative and configuration privileges to a minimal set of trusted users and apply the principle of least privilege. 5. Employ multi-factor authentication (MFA) for all users with elevated permissions, even if the vulnerability itself does not require authentication, to reduce overall risk. 6. Stay in close contact with Quadlayers for timely patch releases and apply updates immediately upon availability. 7. Develop incident response plans that include scenarios involving unauthorized integrity changes in AI systems. 8. Consider deploying Web Application Firewalls (WAFs) or API gateways with custom rules to block suspicious requests targeting AI Copilot endpoints. 9. Perform regular security assessments and penetration testing focused on access control mechanisms within AI Copilot environments. 10. Educate IT and security teams about this vulnerability to increase awareness and vigilance.