CVE-2017-9261 in ImageMagick
Summary
by MITRE
In ImageMagick 7.0.5-6 Q16, the ReadMNGImage function in coders/png.c allows attackers to cause a denial of service (memory leak) via a crafted file.
If you want to get the best quality for vulnerability data then you always have to consider VulDB.
Analysis
by VulDB Data Team • 12/07/2022
The vulnerability identified as CVE-2017-9261 affects ImageMagick version 7.0.5-6 within its Q16 configuration, specifically targeting the ReadMNGImage function located in the coders/png.c source file. This flaw represents a denial of service condition that manifests through memory leak exploitation, allowing malicious actors to consume excessive system resources and potentially render affected systems unavailable. The vulnerability arises from inadequate memory management within the MNG (Multiple Network Graphics) image format processing pipeline, where the application fails to properly release allocated memory blocks during the parsing of malformed MNG files. Such memory leaks can accumulate over time and lead to system instability, particularly when the vulnerable software processes multiple crafted image files in succession. The root cause stems from insufficient input validation and memory deallocation mechanisms within the PNG coder module, which handles the decoding of MNG format images that contain embedded PNG data streams. This vulnerability aligns with CWE-401, which categorizes memory leaks as a critical weakness in software design, and represents a significant concern for systems that process untrusted image content. The attack vector requires an adversary to craft a specially designed MNG file that triggers the memory leak condition when processed by ImageMagick, making this a particularly dangerous flaw in environments where image processing occurs on user-uploaded content or unverified external sources.
The operational impact of CVE-2017-9261 extends beyond simple resource exhaustion, as it can severely compromise system availability and performance across various platforms that rely on ImageMagick for image processing tasks. When exploited, this vulnerability enables attackers to consume memory resources at an accelerated rate, potentially leading to system crashes, application hangs, or complete service disruption. The memory leak occurs during the parsing phase of MNG image files, where the application allocates memory for image data structures but fails to properly free them when encountering malformed input. This behavior creates a cumulative effect that can be particularly devastating in server environments where ImageMagick is used to process high volumes of images, as the memory consumption grows progressively with each processed file. The vulnerability affects systems running ImageMagick 7.0.5-6 Q16, making it a critical concern for organizations that have not yet updated their installations, as the flaw exists in the core image processing functionality that is widely deployed across web applications, content management systems, and digital asset management platforms. The exploitation of this vulnerability can be automated and does not require specialized knowledge of the underlying system architecture, making it particularly dangerous for widespread deployment. From an ATT&CK framework perspective, this vulnerability maps to the T1499.004 technique related to network denial of service, as it can be leveraged to disrupt services through resource exhaustion attacks.
Mitigation strategies for CVE-2017-9261 must address both immediate remediation and long-term architectural improvements to prevent similar vulnerabilities from emerging in image processing pipelines. The most effective immediate solution involves upgrading to ImageMagick versions that have patched this vulnerability, specifically versions released after the 7.0.5-6 release where the memory management issues in the ReadMNGImage function have been addressed. Organizations should implement comprehensive patch management protocols that prioritize security updates for image processing libraries, particularly those handling untrusted input data. Additionally, system administrators should consider implementing input validation measures that filter or reject suspicious image files before they reach the vulnerable processing functions, using file signature checks and content analysis to identify potentially malicious MNG files. Network segmentation and resource monitoring should be implemented to detect unusual memory consumption patterns that may indicate exploitation attempts, allowing for rapid response to potential attacks. The vulnerability also underscores the importance of following secure coding practices such as implementing proper memory deallocation routines, using automated memory leak detection tools during development, and conducting thorough code reviews of image processing components. Organizations should also consider deploying application-level firewalls or content filters that can block known malicious image formats or suspicious file patterns before they reach the ImageMagick processing engine. Regular security audits of image processing workflows are essential to identify potential memory management issues in other parts of the application stack that may present similar vulnerabilities. The remediation process should include comprehensive testing to ensure that the patch does not introduce regressions in legitimate image processing functionality while effectively addressing the memory leak condition.