CVE-2017-12603 in OpenCV
Summary
by MITRE
OpenCV (Open Source Computer Vision Library) through 3.3 has an invalid write in the cv::RLByteStream::getBytes function in modules/imgcodecs/src/bitstrm.cpp when reading an image file by using cv::imread, as demonstrated by the 2-opencv-heapoverflow-fseek test case.
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Analysis
by VulDB Data Team • 12/15/2022
The vulnerability identified as CVE-2017-12603 represents a critical heap-based buffer overflow in the OpenCV computer vision library version 3.3 and earlier. This flaw exists within the cv::RLByteStream::getBytes function located in the modules/imgcodecs/src/bitstrm.cpp file, which is responsible for handling byte stream operations during image file processing. The vulnerability manifests when the library attempts to read image files using the cv::imread function, specifically when processing certain malformed or crafted input files that trigger the overflow condition.
The technical implementation of this vulnerability stems from improper bounds checking within the RLByteStream class, which is designed to handle compressed data streams during image decoding operations. When the function processes input data, it fails to validate the length of data being read from the stream against the allocated buffer size, leading to a situation where more data is written to memory than allocated. This invalid write operation occurs during the decompression and parsing of image metadata, particularly affecting formats that utilize Run-Length encoding or similar compression techniques. The vulnerability is particularly dangerous because it allows an attacker to write data beyond the intended buffer boundaries, potentially corrupting adjacent memory regions and creating opportunities for arbitrary code execution.
The operational impact of this vulnerability extends beyond simple memory corruption, as it represents a serious security risk for any application that relies on OpenCV for image processing tasks. Systems using vulnerable versions of OpenCV are susceptible to remote code execution when processing untrusted image files, making this particularly concerning for web applications, image processing services, and any environment where users can upload or download image content. The heap overflow can be exploited to overwrite critical memory structures, potentially leading to privilege escalation or complete system compromise. This vulnerability aligns with CWE-121, Heap-based Buffer Overflow, and demonstrates characteristics consistent with ATT&CK technique T1203, Exploitation for Client Execution, particularly in environments where image processing is automated.
The attack surface for this vulnerability is extensive given OpenCV's widespread adoption across various platforms and applications, including mobile applications, web services, and enterprise software solutions. The 2-opencv-heapoverflow-fseek test case referenced in the vulnerability description provides a concrete example of how malformed input can trigger the exploitable condition, making it possible for attackers to craft specific image files that will cause the overflow during normal image loading operations. Organizations using OpenCV versions prior to 3.4 should immediately implement mitigations including updating to patched versions, implementing input validation for image files, and deploying sandboxing mechanisms around image processing operations to prevent exploitation. The vulnerability also highlights the importance of proper memory management and bounds checking in multimedia processing libraries, as similar issues can affect other image processing components within the broader software ecosystem.