CVE-2026-68991 in Exynos
Summary
by MITRE • 11/05/2026
In the Samsung Exynos ENN NeuralNetworks HAL, improper input validation during request processing can cause memory corruption and lead to a denial of service condition.
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Analysis
by VulDB Data Team • 11/05/2026
The vulnerability resides within the Samsung Exynos ENN NeuralNetworks Hardware Abstraction Layer (HAL), specifically affecting the component responsible for managing neural network inference requests on devices powered by Exynos processors. This HAL serves as a critical interface between higher-level machine learning frameworks and the underlying hardware accelerators, such as the NPU or GPU, enabling efficient execution of deep learning models. The core technical flaw is characterized by improper input validation during the processing of these request structures. When an application submits a neural network inference task to this layer, the system must parse various parameters including tensor dimensions, data types, memory offsets, and operation counts. In this specific instance, the implementation fails to adequately verify that the provided inputs fall within expected bounds or adhere to structural integrity constraints before proceeding with resource allocation and execution preparation.
This lack of rigorous validation allows for a classic buffer overflow scenario where maliciously crafted input can write beyond the allocated boundaries of internal data structures used by the HAL. Because the NeuralNetworks HAL operates at a privileged level close to the kernel, exploiting this flaw does not necessarily require breaking out of a sandboxed application environment immediately but rather involves manipulating the state of the service handling these requests. An attacker could construct a specially designed inference request that triggers an out-of-bounds write or read operation within the memory space managed by the HAL process. This corruption can overwrite adjacent memory structures, corrupt control flow data such as return addresses on the stack, or damage critical pointers used for subsequent operations.
The operational impact of this vulnerability is primarily centered around service availability and system stability rather than direct remote code execution in its initial manifestation. As noted in the description, the immediate consequence is a denial of service condition. When memory corruption occurs within the HAL process, it typically leads to an application crash or termination by the operating system's watchdog mechanisms designed to prevent unstable processes from affecting other parts of the system. For end-users on mobile devices running Samsung Exynos chips, this manifests as unexpected freezes, crashes in applications utilizing machine learning features like camera enhancements, voice assistants, or image processing tools, and potentially a full device reboot if the crash affects core services dependent on the HAL. While denial of service is the primary documented impact, memory corruption vulnerabilities inherently carry significant risk for further exploitation chains that could lead to privilege escalation or arbitrary code execution if additional conditions are met during runtime.
From a classification perspective, this vulnerability aligns with CWE-20 Improper Input Validation and CWE-119 Memory Buffer Corruption. These categories highlight the failure of the software to correctly check data received from an upstream component before processing it. In terms of offensive security frameworks such as MITRE ATT&CK for Mobile, this behavior is consistent with techniques involving memory corruption exploitation or denial of service attacks against system services. The vulnerability underscores the importance of robust input sanitization in low-level system components that handle complex binary structures like neural network tensors.
Mitigation strategies must focus on both immediate patching and long-term architectural improvements. Samsung should release a security update to the Exynos ENN NeuralNetworks HAL that implements strict boundary checks for all tensor dimensions, memory offsets, and array sizes before they are used in allocation or copy operations. Developers integrating this HAL into their applications can also contribute to mitigation by ensuring that any custom neural network models submitted do not contain malformed metadata. Furthermore, enabling hardware-based security features such as Memory Protection Extensions (MPE) on supported Exynos devices can help detect and terminate processes attempting illegal memory accesses, thereby limiting the impact of successful exploitation attempts. Regular auditing of HAL code against secure coding standards like CERT C or MISRA is essential to prevent similar input validation flaws in future iterations of machine learning infrastructure components.