CVE-2017-12602 in OpenCV
Summary
by MITRE
OpenCV (Open Source Computer Vision Library) through 3.3 has a denial of service (memory consumption) issue, as demonstrated by the 10-opencv-dos-memory-exhaust test case.
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Analysis
by VulDB Data Team • 12/15/2022
The vulnerability identified as CVE-2017-12602 affects the OpenCV library version 3.3 and earlier, representing a significant denial of service weakness that can be exploited through memory consumption. This issue manifests when specific input data triggers excessive memory allocation within the library's processing routines, leading to system resource exhaustion and potential application crashes. The vulnerability was demonstrated through the 10-opencv-dos-memory-exhaust test case which showcased how malformed or specially crafted inputs could cause the library to consume excessive memory resources, ultimately resulting in system instability or complete service unavailability. The flaw resides in the library's handling of certain image processing operations that fail to properly validate input parameters or implement adequate memory management controls, creating an avenue for attackers to systematically consume available system memory through carefully constructed inputs.
The technical implementation of this vulnerability stems from insufficient bounds checking and memory allocation validation within OpenCV's core processing functions. When the library encounters specific input patterns during image or video processing operations, it fails to properly limit the memory allocation requests or implement appropriate input sanitization mechanisms. This weakness creates a condition where the library can be induced to allocate increasingly large memory blocks without proper constraints, effectively exhausting the available system memory. The vulnerability is particularly concerning because it can be triggered through legitimate library usage scenarios, making it difficult to distinguish between normal operation and malicious exploitation. According to CWE classification, this represents a weakness in the form of insufficient resource management or inadequate input validation, specifically categorized under CWE-400 which deals with resource exhaustion vulnerabilities. The attack vector can be executed through various input formats that the library accepts, including image files, video streams, or other media data that the library processes through its computer vision algorithms.
The operational impact of CVE-2017-12602 extends beyond simple service disruption to potentially compromise entire systems that rely on OpenCV for computer vision tasks. Applications using vulnerable versions of the library could become completely unresponsive when processing malicious inputs, leading to denial of service for legitimate users and potential business continuity issues. The memory consumption pattern typically follows a predictable escalation where each processed input causes progressively higher memory allocation, eventually leading to system crashes or the termination of the affected application by the operating system's memory management subsystem. This vulnerability affects a wide range of applications including security systems, robotics platforms, medical imaging software, and any system that incorporates OpenCV for image processing or computer vision tasks. Organizations relying on these systems face significant operational risks, as the vulnerability can be exploited remotely through network-based inputs, making it particularly dangerous for web applications or services that process user-uploaded media files.
Mitigation strategies for CVE-2017-12602 primarily involve upgrading to patched versions of the OpenCV library where the memory management issues have been addressed through proper input validation and resource allocation controls. System administrators should implement immediate patches or updates to all affected systems, as the vulnerability does not require special privileges to exploit and can be triggered through normal library usage patterns. Additionally, deploying input validation layers at network boundaries or application interfaces can help filter out potentially malicious inputs before they reach the vulnerable library components. Organizations should also consider implementing memory monitoring and alerting systems to detect unusual memory consumption patterns that might indicate exploitation attempts. From an ATT&CK framework perspective, this vulnerability aligns with techniques involving resource exhaustion and denial of service operations, specifically categorized under the T1499 sub-technique for network denial of service. The recommended defense-in-depth approach includes maintaining up-to-date software inventories, implementing proper security patch management processes, and conducting regular vulnerability assessments to identify and remediate similar issues within the software supply chain. System architects should also consider implementing sandboxing or containerization strategies to limit the impact of potential exploitation attempts and prevent complete system compromise through memory exhaustion attacks.