CVE-2015-5935 in Mac OS X
Summary
by MITRE
ImageIO in Apple iOS before 9.1, OS X before 10.11.1, and watchOS before 2.0.1 allows remote attackers to execute arbitrary code or cause a denial of service (memory corruption) via crafted metadata in an image, a different vulnerability than CVE-2015-5936, CVE-2015-5937, and CVE-2015-5939.
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Analysis
by VulDB Data Team • 11/19/2024
The vulnerability described in CVE-2015-5935 represents a critical memory corruption flaw within Apple's ImageIO framework that affects multiple operating systems including iOS versions prior to 9.1, macOS versions before 10.11.1, and watchOS versions before 2.0.1. This vulnerability specifically targets the processing of image metadata, which is commonly embedded within image files such as jpeg png and tiff formats. The flaw arises when the ImageIO framework attempts to parse and handle crafted metadata structures that contain malformed or maliciously constructed data elements. This issue falls under the CWE-125 vulnerability category, which represents out-of-bounds read conditions that can lead to memory corruption and arbitrary code execution. The vulnerability demonstrates a classic buffer over-read scenario where the framework fails to properly validate the size and structure of metadata fields before attempting to process them, creating opportunities for attackers to manipulate memory layout and potentially execute malicious code.
The operational impact of this vulnerability extends beyond simple denial of service scenarios to encompass full arbitrary code execution capabilities that could be leveraged by remote attackers. When an attacker crafts an image file with specially designed metadata, the ImageIO framework's processing routines can be tricked into reading memory locations beyond the intended buffer boundaries. This memory corruption can result in unpredictable behavior including application crashes, system instability, or more critically, the ability for attackers to inject and execute malicious code within the context of the affected applications. The vulnerability is particularly concerning because it can be triggered through common image processing operations that occur automatically when users view images or when applications attempt to read image properties for display purposes. The attack vector is remote and can be delivered through various channels including email attachments, web content, or file sharing systems where users might encounter the maliciously crafted images.
From a threat modeling perspective, this vulnerability aligns with several ATT&CK techniques including T1059 for command and script execution, T1203 for exploitation for privilege escalation, and T1190 for exploitation of remote services. The vulnerability enables attackers to potentially escalate privileges by executing code with the same permissions as the vulnerable application, which could be the system's image processing services or user applications that handle image files. The attack surface is broad since many applications across Apple's ecosystem rely on ImageIO for image handling, including photo viewers, web browsers, email clients, and document processors. The exploitation requires minimal user interaction beyond viewing an image or having an application process an image file, making it particularly dangerous in social engineering scenarios where users might unknowingly trigger the vulnerability. Security researchers have noted that the memory corruption pattern suggests the presence of a heap-based buffer overflow that could be leveraged for more sophisticated attacks including return-oriented programming or other exploitation techniques that rely on controlled memory corruption.
Mitigation strategies for CVE-2015-5935 primarily focus on immediate system updates and patches provided by Apple to address the underlying memory corruption issues in the ImageIO framework. Organizations should prioritize deployment of the relevant security updates for iOS 9.1, macOS 10.11.1, and watchOS 2.0.1 releases that contain fixes for this vulnerability. Network security measures including content filtering and image scanning systems can provide additional defense-in-depth layers to prevent potentially malicious images from reaching end users, particularly in enterprise environments where image processing applications are prevalent. Application developers should implement additional input validation and sanitization when handling image metadata, even when relying on system libraries, to provide additional protection against malformed image data. System administrators should monitor for unusual application behavior or memory access patterns that might indicate exploitation attempts, and consider implementing network-based intrusion detection systems that can identify traffic patterns associated with image-based attacks. The vulnerability also highlights the importance of secure coding practices in image processing libraries and the need for comprehensive testing of metadata handling routines to prevent similar memory corruption issues in future implementations.