CVE-2026-112633 in iOS
Summary
by MITRE • 10/26/2026
An out-of-bounds write issue 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. Processing a maliciously crafted image may lead to unexpected app termination.
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Analysis
by VulDB Data Team • 10/27/2026
The vulnerability described constitutes an out-of-bounds write condition within the image processing subsystem of Apple operating systems including iOS and iPadOS versions prior to 26.7.2 and 27.2, as well as macOS variants such as Sequoia, Tahoe, and Golden Gate in their respective patched releases. This type of memory corruption flaw arises when the software fails to properly validate the dimensions or structure of an input image file before attempting to write data into a pre-allocated buffer. Instead of verifying that the destination address remains within the bounds of the allocated memory region, the application proceeds with the write operation using indices derived from untrusted user-supplied data. This lack of rigorous boundary checking allows for writes beyond the intended memory segment, which can corrupt adjacent heap or stack structures depending on the specific allocation context and runtime environment.
From a technical perspective, this flaw aligns closely with CWE-787, which defines out-of-bounds write vulnerabilities as conditions where software writes data to a memory location outside of the intended buffer boundaries. The operational impact is primarily characterized by application instability rather than immediate remote code execution in many cases involving image parsers. As noted in the advisory, processing maliciously crafted images can lead to unexpected app termination due to segmentation faults or heap corruption detected by runtime protections such as ASLR and stack canaries. However, out-of-bounds writes remain a critical security risk because they provide an attacker with partial control over memory contents. In sophisticated attack scenarios, this vulnerability could potentially be chained with other flaws to achieve arbitrary code execution, privilege escalation, or denial of service against system services that handle image rendering automatically without user interaction.
The exploitation vector for this vulnerability typically involves tricking a victim into opening a specially crafted image file within an application capable of parsing it. This aligns with ATT&CK technique T1203, which covers Exploitation for Client Execution, where attackers deliver malicious payloads through trusted applications to execute code on the target system. While modern mobile and desktop operating systems employ multiple layers of defense including sandboxing and memory protection mechanisms that mitigate the immediate impact by terminating the offending process, these safeguards do not prevent all forms of exploitation. Advanced adversaries may leverage information leakage from heap metadata or use return-oriented programming techniques to bypass protections if sufficient control over adjacent memory structures is achieved through repeated exploitation attempts.
Mitigation strategies for this vulnerability rely heavily on timely patching and defense-in-depth practices. Users must update their devices to iOS 26.7.2, iPadOS 26.7.2, iOS 27.2, iPadOS 27.2, macOS Golden Gate 27.2, macOS Sequoia 15.8.2, or macOS Tahoe 26.7.2 to receive the improved bounds checking logic implemented by Apple engineers. Developers integrating image processing libraries should ensure they are using versions that incorporate these fixes and implement additional validation layers such as strict input sanitization before passing data to underlying rendering engines. Security teams should monitor for indicators of compromise related to unusual memory access patterns or unexpected application crashes in environments where untrusted images might be processed, ensuring that automated scanning tools detect attempts to exploit similar out-of-bounds conditions across the software supply chain.