CVE-2016-1516 in OpenCV
Summary
by MITRE
OpenCV 3.0.0 has a double free issue that allows attackers to execute arbitrary code.
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Analysis
by VulDB Data Team • 11/28/2022
The CVE-2016-1516 vulnerability represents a critical double free memory corruption issue within OpenCV version 3.0.0, a widely used computer vision library that forms the foundation for numerous applications in image processing, machine learning, and real-time computer vision systems. This vulnerability specifically affects the library's handling of memory allocation and deallocation processes, creating a scenario where malicious actors can manipulate the memory management subsystem to trigger unintended behavior. The flaw manifests when the library processes certain image files or data structures that contain malformed or specially crafted inputs, leading to the repeated deallocation of the same memory block. This double free condition creates a predictable memory layout that attackers can exploit to overwrite critical memory regions and ultimately execute arbitrary code within the context of the vulnerable application. The vulnerability is particularly concerning because OpenCV is integrated into countless software solutions across multiple domains including security cameras, medical imaging systems, autonomous vehicles, and industrial automation platforms.
The technical implementation of this double free vulnerability stems from improper memory management within OpenCV's internal data structures, particularly affecting how the library handles reference counting and memory cleanup operations for image buffers and data containers. When processing malformed input data, the library's memory management functions fail to properly track memory allocations and deallocations, resulting in a scenario where the same memory address is passed to the free() function twice. This condition creates a heap corruption state that allows attackers to manipulate the heap layout and potentially overwrite function pointers, return addresses, or other critical data structures. The vulnerability is classified under CWE-415 as an improper free or double free condition, which is a well-documented weakness in memory management that has been exploited in numerous security incidents. The attack vector typically involves feeding maliciously crafted image files or data streams to applications that utilize OpenCV, where the library's failure to validate memory operations leads to the exploitable state.
The operational impact of CVE-2016-1516 extends far beyond simple code execution, as it can compromise entire systems that rely on OpenCV for computer vision tasks. Applications affected by this vulnerability include but are not limited to surveillance systems, medical imaging software, robotics platforms, and mobile applications that process visual data. The exploitability of this vulnerability is enhanced by the fact that OpenCV is frequently used in embedded systems and IoT devices where security updates may be infrequent or absent, creating persistent attack surfaces. Attackers can leverage this vulnerability to gain unauthorized access to systems, escalate privileges, or establish persistent backdoors within networked environments. The vulnerability's impact is particularly severe in environments where OpenCV is used for security-critical applications such as facial recognition systems, intrusion detection, or automated license plate recognition systems. The potential for remote code execution makes this vulnerability attractive to threat actors targeting industrial control systems, financial services, and defense applications that rely on computer vision technologies.
Mitigation strategies for CVE-2016-1516 should prioritize immediate patching of affected OpenCV installations, as the vulnerability has been addressed in subsequent releases of the library. Organizations must conduct comprehensive inventory assessments to identify all systems utilizing OpenCV 3.0.0 or earlier versions, particularly focusing on embedded systems and legacy applications that may not receive regular updates. The recommended approach involves upgrading to OpenCV version 3.1.0 or later, which includes proper memory management fixes and additional input validation mechanisms. System administrators should implement network segmentation and access controls to limit exposure of vulnerable applications, while also deploying intrusion detection systems to monitor for exploitation attempts. Additional defensive measures include implementing application whitelisting policies, enabling address space layout randomization, and utilizing heap-based memory protection mechanisms such as stack canaries and data execution prevention. Organizations should also consider implementing runtime monitoring solutions that can detect anomalous memory allocation patterns or heap corruption behaviors that may indicate exploitation attempts. The vulnerability serves as a reminder of the importance of proper memory management in security-critical libraries and highlights the need for comprehensive code reviews and security testing throughout the software development lifecycle.