CVE-2017-12598 in OpenCV
Summary
by MITRE
OpenCV (Open Source Computer Vision Library) through 3.3 has an out-of-bounds read error in the cv::RBaseStream::readBlock function in modules/imgcodecs/src/bitstrm.cpp when reading an image file by using cv::imread, as demonstrated by the 8-opencv-invalid-read-fread test case.
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Analysis
by VulDB Data Team • 12/15/2022
The vulnerability CVE-2017-12598 represents a critical out-of-bounds read flaw within the OpenCV computer vision library version 3.3 and earlier. This issue resides in the cv::RBaseStream::readBlock function located in the modules/imgcodecs/src/bitstrm.cpp file, which is responsible for handling image file parsing operations. The vulnerability manifests when the library processes image files through the cv::imread function, making it particularly concerning given the widespread use of OpenCV in image processing applications across various industries. The specific test case 8-opencv-invalid-read-fread demonstrates how malformed input data can trigger this memory access violation, potentially leading to system instability or exploitation.
The technical nature of this vulnerability falls under CWE-125, which describes "Out-of-bounds Read" conditions where a program accesses memory beyond the boundaries of a buffer. The flaw occurs during the image decoding process when the RBaseStream class attempts to read data blocks without proper bounds checking against the actual file size or structure. This insufficient validation allows an attacker to craft specially formatted image files that cause the readBlock function to access memory locations outside the intended buffer boundaries. The vulnerability is particularly dangerous because it can be triggered through normal image loading operations, requiring no special privileges or complex attack vectors. The out-of-bounds memory access can result in information disclosure, application crashes, or potentially more severe consequences depending on the execution environment.
From an operational perspective, this vulnerability poses significant risks to systems that rely on OpenCV for image processing tasks. The impact extends across multiple domains including security surveillance systems, medical imaging applications, autonomous vehicle software, and any platform that processes user-uploaded images. Attackers could exploit this vulnerability by uploading maliciously crafted image files that would cause applications using OpenCV to crash or behave unpredictably. The vulnerability's trigger mechanism through cv::imread makes it particularly dangerous in web applications or any system where user-provided image files are processed without proper validation. Additionally, the out-of-bounds read could potentially be leveraged in combination with other vulnerabilities to achieve remote code execution, especially in environments where memory corruption leads to exploitable conditions. The vulnerability affects not just individual applications but entire ecosystems built on OpenCV, making it a critical concern for organizations maintaining software that depends on this library.
The mitigation strategy for CVE-2017-12598 involves immediate patching of OpenCV installations to versions 3.4.0 and later, where the bounds checking has been properly implemented. Organizations should conduct comprehensive vulnerability assessments to identify all systems utilizing vulnerable OpenCV versions and prioritize their remediation. Network segmentation and input validation controls should be implemented to prevent malicious image files from reaching systems that process user uploads. Security monitoring should be enhanced to detect unusual patterns in image processing operations that might indicate exploitation attempts. The vulnerability also highlights the importance of proper software supply chain security, emphasizing the need for regular library updates and dependency management. From an ATT&CK framework perspective, this vulnerability aligns with techniques involving initial access through malicious files and privilege escalation through memory corruption, making it a significant concern for defensive security operations. Organizations should also consider implementing runtime protections and memory safety features to reduce the impact of similar vulnerabilities in the future.