CVE-2017-13146 in ImageMagick
Summary
by MITRE
In ImageMagick before 6.9.8-5 and 7.x before 7.0.5-6, there is a memory leak in the ReadMATImage function in coders/mat.c.
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Analysis
by VulDB Data Team • 12/16/2022
The vulnerability CVE-2017-13146 represents a memory leak flaw within ImageMagick's image processing library that affects versions prior to 6.9.8-5 and 7.x prior to 7.0.5-6. This issue specifically resides in the ReadMATImage function located in the coders/mat.c file, which is responsible for handling MATLAB matrix image format processing. The memory leak occurs when ImageMagick attempts to read and process MAT files, which are binary formats used by MATLAB for storing arrays and other data structures. This vulnerability demonstrates a classic software engineering oversight where allocated memory resources are not properly released during the image parsing process, creating a persistent memory consumption issue that can be exploited by malicious actors.
The technical implementation of this memory leak stems from improper memory management within the ReadMATImage function where allocated memory blocks are not consistently freed after processing. When an attacker crafts a malicious MAT file with specific memory allocation patterns, the function fails to properly deallocate previously allocated memory segments, leading to gradual memory accumulation within the application process. This type of vulnerability falls under CWE-401, which specifically addresses improper release of memory resources, and represents a common class of issues in image processing libraries where complex parsing routines must manage multiple memory allocations for different data structures. The vulnerability is particularly concerning because it can be triggered through normal image processing operations without requiring special privileges or complex exploitation techniques.
The operational impact of CVE-2017-13146 extends beyond simple resource exhaustion, as it can lead to denial of service conditions in applications that rely on ImageMagick for image processing. When exploited continuously, the memory leak can cause applications to consume all available system memory, resulting in system instability, application crashes, and potentially complete system hangs. This vulnerability affects a wide range of software applications including web servers, content management systems, and image processing platforms that utilize ImageMagick as their underlying image processing engine. From an attacker's perspective, this vulnerability aligns with ATT&CK technique T1499.004, which involves resource exhaustion attacks, and can be leveraged in automated exploitation campaigns targeting web applications that process user-uploaded images. The vulnerability also maps to ATT&CK technique T1059.007, as it can be exploited through command-line interface interactions when applications invoke ImageMagick for image processing.
Mitigation strategies for CVE-2017-13146 primarily involve updating ImageMagick to versions 6.9.8-5 or 7.0.5-6, which contain the necessary patches to properly release allocated memory resources. Organizations should implement comprehensive patch management procedures to ensure all affected systems receive timely updates, particularly those running web applications or image processing services. Additional defensive measures include implementing input validation and sanitization for image file processing, limiting memory consumption through resource limits, and monitoring application memory usage for unusual patterns. Security teams should also consider implementing network-based detection rules that can identify attempts to exploit this vulnerability through crafted MAT files, as well as establishing automated monitoring for memory leak patterns that could indicate exploitation attempts. The vulnerability serves as a reminder of the critical importance of proper memory management in image processing libraries and the potential for seemingly benign parsing operations to become significant security risks when not properly implemented with adequate resource cleanup mechanisms.