CVE-2017-12864 in OpenCV
Summary
by MITRE
In opencv/modules/imgcodecs/src/grfmt_pxm.cpp, function ReadNumber did not checkout the input length, which lead to integer overflow. 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-2017-12864 resides within the OpenCV computer vision library, specifically in the image codecs module where the ReadNumber function in grfmt_pxm.cpp fails to properly validate input length parameters. This flaw constitutes a classic integer overflow vulnerability that occurs when the function processes image data without adequate bounds checking on the input stream length. The affected component handles PXM (Portable Any Map) image format parsing, which includes PBM (Portable Bit Map), PGM (Portable Gray Map), and PPM (Portable Pixel Map) formats commonly used in computer vision applications and image processing workflows.
The technical implementation of this vulnerability stems from insufficient input validation within the ReadNumber function which is responsible for parsing numeric values from image headers during format detection and parsing operations. When processing malformed PXM image files, the function does not verify that the input data length matches expected parameters, allowing an attacker to craft specially crafted image files that cause integer overflow conditions. This overflow can result in unpredictable behavior where the application may allocate insufficient memory or perform invalid memory operations, creating potential attack vectors for remote code execution or denial of service attacks.
The operational impact of this vulnerability is significant given that OpenCV is widely deployed across various security applications, image processing systems, and computer vision platforms that handle untrusted image data from remote sources. Attackers can exploit this vulnerability by delivering maliciously crafted PXM image files to systems running affected OpenCV versions, potentially leading to remote code execution when the library processes these images in applications such as security cameras, medical imaging systems, or automated surveillance platforms. The vulnerability affects OpenCV versions 3.3 and earlier, representing a substantial attack surface considering the library's extensive deployment in enterprise and security environments.
This vulnerability maps to CWE-190, Integer Overflow or Wraparound, and aligns with ATT&CK technique T1203, Exploitation for Client Execution, when exploited in remote scenarios. The attack vector typically involves a remote user or application processing untrusted image data through the vulnerable OpenCV library, where the integer overflow manifests during image header parsing operations. Mitigation strategies should focus on upgrading to OpenCV versions 3.4.0 or later where this vulnerability has been patched, implementing input validation controls, and deploying network segmentation to limit exposure to potentially malicious image data sources. Organizations should also consider implementing automated image validation routines and restricting image file processing to trusted sources to reduce the risk of exploitation in environments where remote code execution could compromise system integrity or availability.