CVE-2017-12600 in OpenCV
Summary
by MITRE
OpenCV (Open Source Computer Vision Library) through 3.3 has a denial of service (CPU consumption) issue, as demonstrated by the 11-opencv-dos-cpu-exhaust test case.
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Analysis
by VulDB Data Team • 12/15/2022
The vulnerability identified as CVE-2017-12600 represents a critical denial of service flaw within the OpenCV library, specifically affecting versions through 3.3. This issue manifests as excessive cpu consumption that can lead to system resource exhaustion and potential service disruption. The vulnerability was demonstrated through the 11-opencv-dos-cpu-exhaust test case, which showcased how maliciously crafted input data could trigger continuous cpu utilization cycles that overwhelm system resources. OpenCV, being a widely adopted computer vision library used across numerous applications and platforms, creates significant risk when exploited. The flaw resides in the library's handling of certain image processing operations where improper input validation allows for recursive or iterative processing patterns that consume disproportionate computational resources.
The technical root cause of this vulnerability lies in the insufficient input validation and boundary checking within OpenCV's image processing functions. When specific malformed image data is processed, the library enters into infinite or extremely long processing loops where computational resources are consumed at an exponential rate. This behavior aligns with CWE-400, which categorizes issues related to uncontrolled resource consumption, and specifically maps to CWE-778 which addresses insufficient logging of resource consumption. The flaw demonstrates how seemingly benign image processing operations can be weaponized through crafted input data that triggers unintended computational behavior. The vulnerability exploits the library's internal algorithms for image analysis and feature detection, where certain parameter combinations lead to iterative processes that never terminate properly, consuming cpu cycles continuously.
The operational impact of CVE-2017-12600 extends beyond simple system slowdowns to potentially complete system compromise in resource-constrained environments. Attackers can leverage this vulnerability to perform resource exhaustion attacks against systems running OpenCV-based applications, leading to denial of service conditions that affect availability. Systems using OpenCV for real-time image processing, surveillance applications, or automated analysis pipelines become particularly vulnerable as these applications may be exposed to untrusted image data from various sources. The attack surface is broad given OpenCV's extensive use in security cameras, medical imaging systems, automotive vision systems, and industrial automation. This vulnerability can be exploited through common attack vectors such as web uploads, file processing, or network-based image transmission, making it particularly dangerous in multi-tenant environments where one compromised system can affect others. The impact is further amplified when considering that many applications using OpenCV are deployed in critical infrastructure environments where service availability is paramount.
Mitigation strategies for CVE-2017-12600 must address both immediate remediation and long-term architectural improvements. The primary recommendation is to upgrade to OpenCV version 3.3.1 or later, where the vulnerability has been patched through improved input validation and boundary checking mechanisms. Organizations should implement strict input sanitization processes for all image data processed through OpenCV libraries, including size limitations, format validation, and resource consumption monitoring. Network segmentation and access controls should be implemented to limit exposure to untrusted image data sources. Additionally, system monitoring should include cpu utilization thresholds and automated alerting for unusual processing patterns. The vulnerability highlights the importance of following security best practices such as those outlined in the OWASP Top 10 and NIST cybersecurity frameworks, particularly focusing on resource management and input validation controls. Implementing rate limiting and processing timeouts for image analysis operations provides additional defense in depth layers against similar vulnerabilities in other computer vision libraries and applications.