CVE-2026-112629 in iOS
Summary
by MITRE • 10/26/2026
An out-of-bounds read was addressed with improved bounds checking. This issue is fixed in iOS 26.7.2 and iPadOS 26.7.2, iOS 27.2 and iPadOS 27.2, macOS Golden Gate 27.2, macOS Sequoia 15.8.2, macOS Tahoe 26.7.2, visionOS 27.2. Processing a maliciously crafted image may lead to unexpected app termination.
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Analysis
by VulDB Data Team • 10/26/2026
The vulnerability described constitutes an out-of-bounds read condition within the system's image processing subsystems across multiple Apple operating environments including iOS, iPadOS, macOS, and visionOS. This type of memory safety flaw typically arises when a software component fails to properly validate array indices or buffer boundaries before accessing data structures in memory. In this specific instance, the root cause is attributed to insufficient bounds checking during the parsing or rendering of image files. When an application processes media content, it allocates specific regions of memory to hold pixel data and metadata. If the input file contains crafted headers or payloads that indicate dimensions or offsets exceeding the allocated buffer size, the system may attempt to read from memory locations adjacent to the intended buffer. This behavior violates fundamental principles of secure coding by allowing access to sensitive information stored in neighboring memory blocks without proper authorization checks.
From a technical perspective, this flaw aligns with Common Weakness Enumeration category CWE-125, which defines out-of-bounds read vulnerabilities where software reads data past the end or before the beginning of the intended buffer. The exploitation potential lies in the ability to leak arbitrary memory contents from the process space handling the image processing task. While the immediate observable symptom is often an unexpected application termination due to segmentation faults triggered by accessing invalid memory addresses, more sophisticated attacks could potentially leverage this flaw for information disclosure. By carefully crafting input images that trigger specific read operations at precise offsets, a malicious actor might extract sensitive data such as cryptographic keys, session tokens, or other private user information residing in the adjacent memory space of the affected application process.
The operational impact is primarily characterized by denial of service through application crashes when users interact with specially crafted image files via any compatible system component including web browsers, messaging apps, photo galleries, or background services that automatically preview media content. However, the security implications extend beyond simple stability issues because memory corruption vulnerabilities often serve as precursors to more severe exploits such as arbitrary code execution if combined with other weaknesses like heap overflow conditions in subsequent processing stages. The fact that this issue affects a wide range of platforms indicates it resides in shared core libraries responsible for media decoding and rendering, thereby increasing the attack surface across all devices running these operating systems.
To mitigate this risk, users must ensure their devices are updated to the specified patched versions which include improved bounds checking mechanisms within the image processing pipeline. These updates enforce strict validation of input parameters before memory allocation or access operations occur, ensuring that any request for data beyond allocated limits is rejected safely rather than causing a crash or unauthorized read. Security administrators should also implement content filtering solutions at network perimeters to block known malicious file types and monitor endpoint detection systems for unusual process termination patterns associated with media processing services. Additionally, developers integrating third-party image libraries into their applications should verify that those dependencies are up-to-date and adhere to secure coding standards such as OWASP guidelines for input validation and memory management practices outlined in CWE-125 remediation strategies.