CVE-2022-2471 in CS-CV248
Summary
by MITRE • 09/15/2022
Stack-based Buffer Overflow vulnerability in the EZVIZ Motion Detection component as used in camera models CS-CV248, CS-C6N-A0-1C2WFR, CS-DB1C-A0-1E2W2FR, CS-C6N-B0-1G2WF, CS-C3W-A0-3H4WFRL allows a remote attacker to execute remote code on the device. This issue affects: EZVIZ CS-CV248 versions prior to 5.2.3 build 220725. EZVIZ CS-C6N-A0-1C2WFR versions prior to 5.3.0 build 220428. EZVIZ CS-DB1C-A0-1E2W2FR versions prior to 5.3.0 build 220802. EZVIZ CS-C6N-B0-1G2WF versions prior to 5.3.0 build 220712. EZVIZ CS-C3W-A0-3H4WFRL versions prior to 5.3.5 build 220723.
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Analysis
by VulDB Data Team • 10/18/2022
The CVE-2022-2471 vulnerability represents a critical stack-based buffer overflow flaw within the EZVIZ Motion Detection component of several surveillance camera models. This vulnerability resides in the firmware implementation of network-connected security devices that are widely deployed in both commercial and residential environments. The flaw specifically manifests in the processing of motion detection parameters, where insufficient input validation allows malicious data to overflow allocated stack memory regions. The affected devices include multiple EZVIZ camera models including CS-CV248, CS-C6N-A0-1C2WFR, CS-DB1C-A0-1E2W2FR, CS-C6N-B0-1G2WF, and CS-C3W-A0-3H4WFRL, all of which are susceptible to remote exploitation without authentication requirements. The vulnerability's impact extends beyond simple denial of service as it enables complete system compromise through remote code execution capabilities.
This buffer overflow vulnerability operates through a classic stack-based exploitation technique where attacker-controlled data exceeds the bounds of a fixed-size buffer allocated on the program's stack. The flaw is classified under CWE-121 Stack-based Buffer Overflow, which occurs when a program writes data beyond the boundaries of a stack-allocated buffer. The specific implementation issue lies in how the EZVIZ Motion Detection component processes incoming network requests containing motion detection configuration parameters. When these parameters exceed the expected buffer size, adjacent memory locations become overwritten, potentially corrupting the stack's return addresses and control flow information. The vulnerability is particularly concerning because it allows remote code execution without requiring any authentication credentials, making it accessible to attackers anywhere on the internet.
The operational impact of CVE-2022-2471 is severe and multifaceted, affecting organizations that rely on these surveillance cameras for security monitoring and perimeter protection. Attackers exploiting this vulnerability can gain full administrative control over affected devices, potentially enabling them to view live camera feeds, modify recording settings, disable motion detection alerts, or even install persistent backdoors for long-term access. The vulnerability's remote exploitation capability means that attackers can compromise devices from outside the network perimeter, eliminating the need for physical access or network infiltration. Additionally, compromised cameras can serve as launching points for further attacks within the network, as these devices often have access to sensitive internal systems and can be used to establish command and control channels. The affected firmware versions indicate that this vulnerability has existed for an extended period, leaving many organizations exposed to potential compromise.
Mitigation strategies for CVE-2022-2471 should prioritize immediate firmware updates from EZVIZ, with affected devices being updated to versions 5.2.3 build 220725 or later for CS-CV248 models and 5.3.0 build 220428 or later for the other affected models. Network segmentation and access control should be implemented to limit exposure of these devices to untrusted networks, with firewall rules restricting unnecessary traffic to the affected camera ports. Regular security assessments should be conducted to identify any remaining vulnerable devices within the network infrastructure, as the attack surface expands with interconnected IoT devices. The vulnerability aligns with ATT&CK technique T1071.004 Application Layer Protocol: DNS, as attackers may use DNS tunneling to communicate with compromised devices, and T1059.001 Command and Scripting Interpreter: PowerShell, when executing malicious payloads on compromised systems. Organizations should also implement network monitoring to detect unusual traffic patterns that may indicate exploitation attempts, particularly focusing on unexpected connections to the affected camera models and anomalous data flows that could indicate data exfiltration activities.