CVE-2017-18022 in ImageMagick
Summary
by MITRE
In ImageMagick 7.0.7-12 Q16, there are memory leaks in MontageImageCommand in MagickWand/montage.c.
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Analysis
by VulDB Data Team • 01/20/2023
The vulnerability identified as CVE-2017-18022 represents a critical memory management flaw within ImageMagick's MagickWand library, specifically affecting version 7.0.7-12 with Q16 quantization. This issue manifests in the MontageImageCommand function which is responsible for creating montage images by arranging multiple input images in a grid pattern. The memory leak occurs during the processing of image montage operations, where allocated memory blocks are not properly released back to the system after the montage command completes its execution. This flaw affects the core functionality of ImageMagick's command-line interface and API-based image processing capabilities, particularly when users or applications invoke montage operations on potentially malicious or malformed image files.
The technical root cause of this vulnerability lies in improper memory deallocation within the montage.c source file, where the MontageImageCommand function fails to correctly free dynamically allocated memory structures that are created during the montage processing pipeline. When ImageMagick processes montage commands, it allocates memory for various image data structures, temporary buffers, and metadata containers that are essential for the montage operation. However, due to a missing or incorrect free() operation, these memory allocations persist in the system's heap even after the command execution completes. This memory leak pattern can be classified as a CWE-401: Improper Release of Memory Before Removing Last Reference, which is a well-documented weakness in memory management practices. The vulnerability is particularly concerning because it can be exploited through crafted image files that trigger the montage command, allowing attackers to consume system resources progressively over time.
The operational impact of this memory leak vulnerability extends beyond simple resource consumption, as it can lead to significant system instability and potential denial of service conditions. When an application or service using ImageMagick's MontageImageCommand processes multiple montage operations, each execution leaves behind unreleased memory segments that accumulate over time. This progressive memory consumption can eventually exhaust available system memory, leading to system slowdowns, application crashes, or complete system hangs. The vulnerability is particularly dangerous in server environments where ImageMagick is used for automated image processing, such as web applications, content management systems, or image processing pipelines, where continuous montage operations could be exploited to gradually degrade system performance. The memory leak behavior aligns with ATT&CK technique T1499.001: Endpoint Denial of Service, as it represents a method of resource exhaustion that can render systems unusable through sustained memory consumption.
Mitigation strategies for CVE-2017-18022 require immediate patching of affected ImageMagick installations to version 7.0.7-13 or later, which contains the corrected memory deallocation logic. System administrators should prioritize updating ImageMagick components across all environments where the library is deployed, particularly in web-facing applications and automated processing systems. Additionally, implementing proper input validation and sanitization measures can help prevent exploitation by ensuring that only expected image formats and parameters are processed through montage operations. Organizations should also consider implementing memory monitoring and alerting systems to detect unusual memory consumption patterns that could indicate exploitation attempts. The vulnerability demonstrates the importance of proper memory management in image processing libraries and highlights the need for comprehensive testing of resource allocation and deallocation routines, especially in components that handle user-provided data through complex processing pipelines.