CVE-2017-13060 in ImageMagick
Summary
by MITRE
In ImageMagick 7.0.6-5, a memory leak vulnerability was found in the function ReadMATImage in coders/mat.c, which allows attackers to cause a denial of service via a crafted file.
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Analysis
by VulDB Data Team • 12/16/2022
The vulnerability identified as CVE-2017-13060 represents a critical memory leak flaw within ImageMagick's MAT file format handling mechanism. This issue specifically affects ImageMagick version 7.0.6-5 and resides within the ReadMATImage function located in the coders/mat.c source file. The memory leak occurs when processing specially crafted MATLAB file format files, which are commonly used for storing matrix data and scientific computing results. Attackers can exploit this weakness by preparing malicious MAT files that trigger the vulnerable code path, leading to uncontrolled memory consumption within the application. The vulnerability demonstrates a classic improper resource management issue that falls under CWE-401, which specifically addresses memory leaks and resource leaks in software systems.
The technical exploitation of this vulnerability involves the manipulation of MAT file structures to cause the ReadMATImage function to allocate memory without proper subsequent deallocation. When ImageMagick processes these malformed files, the memory allocation requests continue to accumulate without being properly freed, eventually exhausting the available memory resources on the target system. This memory consumption pattern creates a predictable denial of service condition where legitimate users or processes cannot access the system due to resource exhaustion. The vulnerability is particularly concerning because MAT files are commonly used in scientific and engineering environments, making the attack surface broader than initially apparent. The flaw essentially creates a memory consumption loop that can be sustained indefinitely, allowing attackers to maintain resource exhaustion attacks for extended periods.
From an operational perspective, this vulnerability poses significant risks to systems that process user-uploaded files or handle automated file processing workflows. The denial of service impact can be severe in environments where ImageMagick is used as a core component for image processing, such as web applications, content management systems, or scientific computing platforms. The vulnerability can be exploited remotely through file upload mechanisms or via automated processing pipelines that handle external file inputs. The attack requires minimal sophistication as it only necessitates the creation of a specially crafted MAT file rather than complex exploitation techniques. This makes the vulnerability particularly dangerous because it can be leveraged by attackers with limited technical expertise to disrupt services. The memory leak pattern also makes detection challenging since it may not immediately manifest as a clear error condition but rather as gradual system performance degradation.
The mitigation strategies for this vulnerability primarily involve immediate software updates to versions that contain the patched implementation of the ReadMATImage function. Users should upgrade to ImageMagick versions that address this specific memory leak issue and have undergone thorough testing to ensure the fix does not introduce regressions. Additionally, implementing proper input validation and file type checking mechanisms can provide defense-in-depth measures to prevent malformed MAT files from reaching the vulnerable code path. Organizations should also consider implementing resource limits and monitoring for memory consumption patterns to detect potential exploitation attempts. The vulnerability highlights the importance of proper memory management practices and regular security updates, particularly for widely-used libraries that process untrusted input data. From an ATT&CK framework perspective, this vulnerability maps to the T1499.004 technique related to network denial of service attacks, while also demonstrating the broader category of T1211 defense evasion through resource exhaustion attacks.