CVE-2017-12604 in OpenCV
Summary
by MITRE
OpenCV (Open Source Computer Vision Library) through 3.3 has an out-of-bounds write error in the FillUniColor function in utils.cpp when reading an image file by using cv::imread.
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Analysis
by VulDB Data Team • 12/15/2022
The vulnerability identified as CVE-2017-12604 represents a critical out-of-bounds write flaw within the OpenCV library that affects versions through 3.3. This issue resides in the FillUniColor function located in the utils.cpp source file, where improper input validation during image file processing creates a condition that allows malicious actors to exploit memory corruption. The vulnerability specifically manifests when the cv::imread function processes certain malformed image files, creating an opportunity for arbitrary code execution or system instability. This flaw demonstrates a classic buffer overflow condition where the application writes data beyond the allocated memory boundaries, potentially leading to unpredictable behavior and security breaches.
The technical implementation of this vulnerability stems from insufficient bounds checking within the image parsing logic of OpenCV's utility functions. When cv::imread encounters specific image formats or corrupted image data, the FillUniColor function fails to properly validate array indices or buffer sizes before writing data to memory locations. This type of flaw aligns with CWE-129, which describes improper validation of array indices, and represents a fundamental failure in input sanitization. The vulnerability operates at the intersection of image processing and memory management, where the library's assumption about image data structure leads to incorrect memory access patterns. Attackers can craft malicious image files that, when processed by vulnerable OpenCV versions, trigger the out-of-bounds write condition.
The operational impact of CVE-2017-12604 extends beyond simple memory corruption, as it creates opportunities for privilege escalation and remote code execution in systems that utilize OpenCV for image processing tasks. Systems running applications that depend on OpenCV for image handling, including security cameras, medical imaging systems, and computer vision applications, become vulnerable to exploitation. The vulnerability can be leveraged through various attack vectors including web applications that accept image uploads, email attachment processing systems, or any application that automatically processes image files without proper validation. From an adversary perspective, this flaw aligns with ATT&CK technique T1059.007 for command and script interpreter, as successful exploitation could lead to full system compromise through code execution in the context of the vulnerable application. The widespread adoption of OpenCV across numerous platforms and applications amplifies the potential impact of this vulnerability.
Mitigation strategies for CVE-2017-12604 primarily focus on immediate version updates and input validation enhancements. Organizations should prioritize upgrading to OpenCV versions that contain the patched FillUniColor function, typically those released after the vulnerability disclosure. Additionally, implementing robust image file validation at application boundaries can help prevent exploitation attempts by rejecting malformed or suspicious image data before it reaches the OpenCV processing functions. Security measures should include input sanitization, file format validation, and proper error handling to prevent the propagation of malformed image data into vulnerable library functions. Network-level protections such as web application firewalls and content filtering systems can provide additional layers of defense by detecting and blocking malicious image files. The vulnerability serves as a reminder of the importance of proper memory management in computer vision libraries and highlights the need for comprehensive security testing of image processing components within software applications.