CVE-2017-12862 in OpenCV
Summary
by MITRE
In modules/imgcodecs/src/grfmt_pxm.cpp, the length of buffer AutoBuffer _src is small than expected, which will cause copy buffer overflow later. If the image is from remote, may lead to remote code execution or denial of service. This affects Opencv 3.3 and earlier.
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Analysis
by VulDB Data Team • 12/15/2022
The vulnerability identified as CVE-2017-12862 represents a critical buffer overflow condition within the OpenCV image processing library that affects versions 3.3 and earlier. This flaw exists in the grfmt_pxm.cpp module which handles various pixel map image formats including PBM, PGM, and PPM file types. The vulnerability stems from an insufficiently sized AutoBuffer _src variable that fails to accommodate the expected data length during image processing operations, creating a scenario where maliciously crafted image files can trigger memory corruption issues.
The technical implementation of this vulnerability involves the improper handling of image data buffers during the parsing of pixel map formats. When OpenCV processes image files through the affected module, the AutoBuffer _src is allocated with inadequate memory space to handle the actual data contained within the image file. This buffer sizing error occurs during the image decoding process where the system attempts to copy image data into the pre-allocated buffer without proper bounds checking or dynamic memory reallocation. The flaw manifests as a classic stack-based buffer overflow condition that can be exploited when processing malformed image files, particularly those originating from untrusted sources.
The operational impact of CVE-2017-12862 extends beyond simple denial of service to potentially enable remote code execution, making it a severe security concern for systems utilizing OpenCV for image processing. When a remote attacker crafts a specially formatted pixel map image file, they can manipulate the buffer size calculations to cause memory corruption that may result in arbitrary code execution on the target system. The vulnerability is particularly dangerous because it can be triggered through automated processing of images from untrusted sources, such as email attachments, web downloads, or file sharing systems. This creates a significant attack surface for threat actors who can leverage the vulnerability to gain unauthorized access to systems running vulnerable OpenCV versions.
The exploitability of this vulnerability aligns with ATT&CK technique T1203 by enabling adversaries to perform remote code execution through software exploitation, while the buffer overflow condition maps to CWE-121, which describes heap-based buffer overflow conditions. The vulnerability demonstrates a classic lack of proper input validation and memory management practices that are fundamental to secure coding standards. Systems using OpenCV for image processing, including applications in surveillance, medical imaging, and computer vision, are at risk when operating with vulnerable versions. The attack vector is particularly concerning in enterprise environments where automated image processing workflows may inadvertently process malicious files without proper sanitization.
Organizations should implement immediate mitigations including upgrading to OpenCV version 3.4.0 or later where the buffer overflow has been addressed through proper memory allocation and bounds checking. The fix involves implementing dynamic buffer sizing that accounts for the actual data requirements during image processing operations, rather than using fixed-size buffers that may not accommodate all valid image formats. Additionally, deploying network-based security controls such as content filtering and image sanitization systems can provide additional defense-in-depth layers. Regular security assessments should verify that all systems utilizing OpenCV are running patched versions and that proper input validation is implemented in applications that process external image files. The vulnerability serves as a reminder of the importance of proper memory management and input validation in security-critical software components, particularly those handling untrusted data from external sources.