CVE-2025-47821 identifies a security vulnerability in Flock Safety Gunshot Detection devices, specifically versions prior to 1.3. The issue is classified under CWE-259, which pertains to the use of hard-coded passwords within the system. A hard-coded password is embedded directly into the device's firmware or software, making it static and unchangeable by end users. This vulnerability allows an attacker with network access to the device to potentially authenticate using this known password, bypassing normal authentication mechanisms. The CVSS 3.1 base score is 2.2, indicating a low severity primarily due to the attack vector being physical or local (AV:P), high attack complexity (AC:H), no privileges required (PR:N), and no user interaction (UI:N). The scope is changed (S:C), meaning the vulnerability affects resources beyond the initially vulnerable component. The impact on confidentiality is low (C:L), with no impact on integrity (I:N) or availability (A:N). Although the vulnerability does not currently have known exploits in the wild, the presence of a hard-coded password is a significant security weakness that could be leveraged in targeted attacks or insider threats. The lack of a patch link suggests that a fix may not yet be publicly available or that users should upgrade to version 1.3 or later to remediate the issue. Given the device's role in gunshot detection, which is critical for public safety and law enforcement, unauthorized access could lead to manipulation or disabling of detection capabilities, undermining trust and operational effectiveness.

For European organizations, particularly law enforcement agencies, municipal governments, and private security firms deploying Flock Safety Gunshot Detection devices, this vulnerability poses a risk to the confidentiality and reliability of gunshot event data. An attacker exploiting the hard-coded password could gain unauthorized access to the device, potentially allowing them to intercept sensitive data or manipulate device settings. Although the CVSS score indicates low severity, the operational impact could be significant if attackers disable or tamper with detection capabilities, leading to delayed or missed responses to gunfire incidents. This could undermine public safety efforts and erode community trust in surveillance technologies. Additionally, unauthorized access could expose sensitive location and timing data of gunshot events, raising privacy concerns under the GDPR framework. The vulnerability's exploitation could also serve as a foothold for lateral movement within organizational networks if the devices are connected to broader IT infrastructure.

Organizations should prioritize upgrading all Flock Safety Gunshot Detection devices to version 1.3 or later, where this vulnerability is addressed. In the absence of an official patch, network-level mitigations should be implemented, including isolating these devices on segmented, access-controlled networks to limit exposure. Employ strict firewall rules to restrict management interface access to trusted IP addresses only. Regularly audit device configurations and monitor logs for unauthorized access attempts. Employ network intrusion detection systems (NIDS) to detect anomalous traffic patterns targeting these devices. Additionally, organizations should engage with the vendor to obtain official patches or firmware updates and verify the removal of hard-coded credentials. For new deployments, ensure that devices do not use hard-coded passwords and support secure authentication mechanisms such as unique per-device credentials or integration with centralized authentication services. Finally, incorporate these devices into broader security monitoring and incident response plans to quickly detect and respond to potential compromises.