CVE-2016-1517 in OpenCV
Summary
by MITRE
OpenCV 3.0.0 allows remote attackers to cause a denial of service (segfault) via vectors involving corrupt chunks.
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Analysis
by VulDB Data Team • 11/28/2022
The vulnerability identified as CVE-2016-1517 affects OpenCV version 3.0.0 and represents a critical denial of service flaw that can be exploited by remote attackers to trigger segmentation faults within the image processing library. This issue stems from insufficient input validation mechanisms within the library's memory management and chunk processing routines, which fail to properly handle malformed or corrupted data structures during image decoding operations. The flaw specifically manifests when the library encounters corrupt chunks in image files or data streams, leading to unpredictable memory access patterns that ultimately result in system crashes and service unavailability.
The technical implementation of this vulnerability resides in the memory allocation and deallocation processes within OpenCV's image processing pipeline, where the library does not adequately validate the integrity of chunk headers or data boundaries before attempting to process them. This weakness aligns with CWE-125, which describes out-of-bounds read conditions, and CWE-787, which covers out-of-bounds write vulnerabilities that can occur when processing malformed input data. The vulnerability operates by exploiting the library's failure to implement proper bounds checking mechanisms during chunk parsing, allowing attackers to craft specially formatted image files or data streams that contain corrupt chunk structures. When the vulnerable OpenCV version processes these malicious inputs, the library's internal memory management routines encounter invalid chunk boundaries or corrupted metadata that leads to improper memory access patterns, ultimately causing segmentation faults and system crashes.
From an operational perspective, this vulnerability presents significant risks to systems that rely on OpenCV for image processing tasks, particularly in web applications, content management systems, or any environment where user-uploaded images are processed. Attackers can leverage this flaw to perform remote denial of service attacks against systems running vulnerable versions of OpenCV, potentially causing widespread service disruption and availability issues. The impact extends beyond simple service interruption as it can affect critical infrastructure components that depend on image processing capabilities, including surveillance systems, medical imaging applications, and automated analysis platforms. The vulnerability's remote exploitability means that attackers do not require physical access or local privileges to trigger the flaw, making it particularly dangerous in networked environments where image processing services are exposed to untrusted input sources.
The mitigation strategies for CVE-2016-1517 primarily involve upgrading to patched versions of OpenCV where the memory management and input validation mechanisms have been enhanced to properly handle corrupt chunk structures. Organizations should prioritize updating their OpenCV installations to versions that include improved bounds checking and robust error handling for chunk processing operations. Additionally, implementing input sanitization measures at the application level can provide additional defense-in-depth protection by validating image file integrity before passing them to the OpenCV library. Security monitoring should be enhanced to detect unusual patterns in image processing requests that might indicate exploitation attempts, while network segmentation and access controls can limit the potential impact of successful attacks. The vulnerability also highlights the importance of following secure coding practices as outlined in the OWASP Secure Coding Practices and the MITRE ATT&CK framework's defensive strategies for memory corruption vulnerabilities, emphasizing the need for comprehensive input validation and robust error handling throughout software development lifecycles.