CVE-2025-36906 in Android
Summary
by MITRE • 09/04/2025
In ConvertReductionOp of darwinn_mlir_converter_aidl.cc, there is a possible out of bounds write due to a heap buffer overflow. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.
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Analysis
by VulDB Data Team • 09/04/2025
The vulnerability identified as CVE-2025-36906 represents a critical heap buffer overflow condition within the ConvertReductionOp function of the darwinn_mlir_converter_aidl.cc source file. This flaw manifests as an out of bounds write operation that occurs during the processing of machine learning operations within the MLIR (Multi-Level Intermediate Representation) conversion framework. The vulnerability is particularly concerning because it enables local privilege escalation without requiring any additional execution privileges or user interaction, making it highly exploitable in targeted attack scenarios. The buffer overflow occurs in the context of Android Interface Definition Language (AIDL) conversion processes that handle machine learning model transformations, suggesting this affects systems utilizing Google's Darwinn AI accelerator framework for mobile and embedded device processing.
The technical implementation of this vulnerability stems from inadequate bounds checking within the ConvertReductionOp function where heap allocated memory is accessed beyond its allocated boundaries. This type of flaw typically occurs when array indices or buffer sizes are not properly validated before memory operations are performed, allowing attackers to write data beyond the intended memory allocation. The vulnerability specifically affects the MLIR conversion process that translates high-level machine learning operations into optimized intermediate representations for execution on Darwinn hardware accelerators. According to CWE classification, this represents a variant of CWE-121, Heap-based Buffer Overflow, which falls under the broader category of memory safety issues. The flaw demonstrates characteristics consistent with the ATT&CK technique T1068, Exploitation for Privilege Escalation, as it allows local attackers to gain elevated privileges through memory corruption.
The operational impact of this vulnerability extends beyond simple memory corruption, as it creates a pathway for attackers to execute arbitrary code with elevated privileges on affected systems. Since no user interaction is required for exploitation, this vulnerability can be leveraged in automated attack scenarios where an attacker has local access to a device running the vulnerable software. The affected environment typically includes Android devices utilizing Google's Darwinn AI processing framework, particularly those running versions of Android that incorporate the vulnerable MLIR conversion components. The privilege escalation aspect means that an attacker who gains access to a low-privilege account could potentially elevate their privileges to system-level access, enabling complete compromise of the device's security posture.
Mitigation strategies for CVE-2025-36906 should prioritize immediate patching of affected systems through official software updates from device manufacturers and Google. Organizations should implement monitoring for suspicious memory access patterns and privilege escalation attempts on systems utilizing the affected MLIR conversion components. System administrators should also consider implementing additional security controls such as address space layout randomization and stack canaries to make exploitation more difficult. The vulnerability highlights the importance of rigorous input validation and bounds checking in memory management operations, particularly within complex systems that handle machine learning model conversions. Security teams should also conduct thorough vulnerability assessments of their MLIR-based systems and ensure that all components are regularly updated to prevent similar issues from arising in other parts of the software stack. Given the nature of the vulnerability, it is recommended that organizations implement comprehensive security testing procedures that include memory safety analysis and fuzzing of MLIR conversion processes to identify similar issues before they can be exploited.