CVE-2023-35655 in Android
Summary
by MITRE • 10/25/2023
In CanConvertPadV2Op of darwinn_mlir_converter_aidl.cc, there is a possible out of bounds read due to a heap buffer overflow. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation.
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Analysis
by VulDB Data Team • 10/31/2023
The vulnerability identified as CVE-2023-35655 resides within the CanConvertPadV2Op function of the darwinn_mlir_converter_aidl.cc file, representing a critical heap buffer overflow condition that exposes systems to potential privilege escalation attacks. This flaw manifests as an out of bounds read operation that occurs during the processing of machine learning inference operations within the Android platform's neural network inference framework. The vulnerability is particularly concerning as it operates at the system level where it can be exploited to gain elevated privileges without requiring any user interaction, making it especially dangerous in environments where system-level access is typically restricted.
The technical implementation of this vulnerability stems from improper bounds checking within the buffer management logic of the MLIR converter component. When processing padding operations for neural network computations, the system fails to validate array indices against allocated buffer boundaries, allowing malicious input data to access memory regions beyond the intended buffer limits. This condition creates a classic heap buffer overflow scenario where adjacent memory locations can be read or potentially overwritten, providing attackers with opportunities to execute arbitrary code with system privileges. The flaw operates within the Android Neural Networks API framework, specifically affecting devices that utilize the darwinn inference engine for machine learning workloads.
The operational impact of CVE-2023-35655 extends beyond simple memory corruption as it enables local privilege escalation attacks that can compromise the entire system. Attackers exploiting this vulnerability can leverage the heap buffer overflow to execute code with system-level privileges, potentially gaining access to sensitive system resources, modifying critical system files, or establishing persistent backdoors. The lack of user interaction requirement makes this vulnerability particularly dangerous as it can be exploited automatically without any user involvement, allowing for silent compromise of devices. This type of vulnerability directly maps to CWE-125 Out-of-bounds Read and CWE-787 Out-of-bounds Write, representing fundamental memory safety issues that can be leveraged for privilege escalation attacks.
Security professionals should consider this vulnerability in relation to the ATT&CK framework's privilege escalation tactics, specifically focusing on the use of system-level exploits and memory corruption techniques. The vulnerability aligns with techniques such as T1068 Exploitation for Privilege Escalation and T1059 Command and Scripting Interpreter, as exploitation requires leveraging the system's own execution mechanisms to achieve elevated privileges. Organizations should prioritize patching this vulnerability through official Android security updates, as the affected darwinn_mlir_converter_aidl.cc component represents a core system component that handles neural network operations across various Android devices. The mitigation strategy should include immediate deployment of security patches and monitoring for potential exploitation attempts, particularly in environments where Android devices handle sensitive data processing tasks.