CVE-2026-110455 in UniFi Protect AI Key
Summary
by MITRE • 10/14/2026
A malicious actor with access to the network and under certain conditions could exploit an Improper Input Validation vulnerability found in UniFi Protect AI Key to execute a Command Injection on the device.
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Analysis
by VulDB Data Team • 10/14/2026
The discovery of a command injection vulnerability within the UniFi Protect AI Key represents a critical security flaw that undermines the integrity of edge-based artificial intelligence processing hardware. This specific weakness stems from improper input validation, where the system fails to adequately sanitize or verify user-supplied data before it is processed by underlying operating system commands. In environments utilizing this device for video analytics and motion detection, such as commercial surveillance setups or high-security residential installations, the AI Key serves as a dedicated processor offloading tasks from main cameras. The vulnerability allows an attacker who has gained network access to inject arbitrary shell commands into these processing workflows, effectively bypassing intended security boundaries and gaining unauthorized control over the device's execution environment.
From a technical perspective, this flaw aligns with CWE-78, which classifies improper neutralization of special elements used in an operating system command, commonly known as OS Command Injection. The root cause typically involves concatenating unsanitized user input directly into a system call string without employing rigorous escaping mechanisms or parameterized execution methods. When the AI Key processes specific inputs related to its machine learning models or configuration parameters, it may pass these values directly to shell interpreters like sh or bash. If an attacker can manipulate these inputs through network-facing interfaces such as REST APIs, WebSocket connections, or management protocols, they can terminate existing command strings and append malicious payloads. This technique leverages the trust relationship between the application layer and the operating system kernel, allowing for arbitrary code execution with the privileges of the service running the AI processing daemon.
The operational impact of this vulnerability is severe, particularly given the sensitive nature of video surveillance data. An attacker exploiting this command injection can achieve remote code execution on the device itself. This capability enables the compromise of the entire network segment if the AI Key has broader access permissions or serves as a pivot point for lateral movement. Beyond direct control, an adversary could exfiltrate stored configuration files containing encryption keys or authentication tokens, modify firmware to install persistent backdoors, or disrupt video analytics services by crashing critical processes. In a corporate environment, this could lead to significant privacy violations and regulatory non-compliance under frameworks such as GDPR or HIPAA if protected health information is captured in the surveillance footage. Furthermore, the compromised device can be recruited into botnets for distributed denial-of-service attacks, leveraging its network connectivity and processing power against other targets.
Mitigation strategies must address both immediate remediation and long-term architectural resilience. The primary defense involves applying vendor-provided firmware updates that patch the input validation logic by implementing strict allow-listing of acceptable characters and using safe API calls instead of shell interpreters for system operations. Network segmentation is crucial; the AI Key should reside in a isolated VLAN with restricted access controls, ensuring only authorized management stations can communicate with it. Additionally, deploying intrusion detection systems configured to detect command injection patterns in network traffic can provide early warning indicators of exploitation attempts. Security teams should also enforce strong authentication mechanisms and disable unused services or ports on the device to reduce the attack surface. Regular vulnerability assessments and penetration testing focused on input validation flaws are essential to maintain a robust security posture against evolving threats targeting IoT and AI-enabled infrastructure components.