CVE-2018-4016 in A1 Dashcam
Summary
by MITRE
An exploitable code execution vulnerability exists in the URL-parsing functionality of the Roav A1 Dashcam running version RoavA1SWV1.9. A specially crafted packet can cause a stack-based buffer overflow, resulting in code execution. An attacker can send a packet to trigger this vulnerability.
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Analysis
by VulDB Data Team • 09/17/2023
The CVE-2018-4016 vulnerability represents a critical stack-based buffer overflow flaw within the Roav A1 Dashcam device firmware version RoavA1SWV1.9. This vulnerability specifically targets the URL-parsing functionality of the device, which serves as a fundamental component for network communications and remote management capabilities. The affected device operates as a dashcam system that likely connects to cloud services or remote monitoring platforms, making its network parsing functionality a prime target for exploitation. The vulnerability arises from improper input validation within the URL processing code, where user-supplied data is not adequately sanitized before being processed.
The technical implementation of this flaw demonstrates a classic stack buffer overflow condition where maliciously crafted URL data can exceed the allocated buffer space, overwriting adjacent memory locations including return addresses and control data. This type of vulnerability falls under CWE-121 Stack-based Buffer Overflow, which is classified as a high-severity issue in the Common Weakness Enumeration catalog. The overflow occurs during the parsing of network requests, particularly when handling URLs that contain specially crafted payloads designed to exceed buffer boundaries. Attackers can exploit this by sending maliciously formatted network packets containing oversized URL parameters that trigger the buffer overflow condition.
The operational impact of this vulnerability extends beyond simple code execution, as it provides attackers with complete control over the affected device. Once successfully exploited, the attacker can execute arbitrary code with the privileges of the running process, potentially leading to full system compromise. This includes the ability to install malicious software, modify device configurations, access stored data such as recorded video footage, and potentially use the device as a pivot point for attacking other systems on the same network. The dashcam's network connectivity makes it particularly attractive for attackers seeking persistent access points or as part of larger network infiltration campaigns.
From a threat modeling perspective, this vulnerability aligns with ATT&CK technique T1059.007 for command and script interpreter and T1071.004 for application layer protocol. The attack surface is particularly concerning given that dashcams often operate in unsecured environments where physical access is limited but network access may be possible. The vulnerability is exploitable remotely without authentication, making it a significant risk for devices deployed in vehicles or other mobile environments where they may be exposed to network-based attacks. Organizations should consider implementing network segmentation and monitoring for unusual URL parsing patterns to detect potential exploitation attempts.
Mitigation strategies for CVE-2018-4016 should focus on immediate firmware updates from Roav, as this represents a vendor-specific fix. Network-level protections including firewalls and intrusion detection systems should be configured to monitor and block suspicious URL patterns. Device administrators should also consider disabling unnecessary network services and implementing strict access controls for remote management functions. The vulnerability highlights the importance of input validation and proper memory management in embedded systems, particularly those with network connectivity. Organizations should conduct vulnerability assessments of their entire fleet of dashcams and similar IoT devices to identify similar weaknesses in other network-parsing components. Additionally, implementing network monitoring solutions that can detect anomalous URL patterns or excessive data transfers may provide early warning of exploitation attempts.