CVE-2023-26085: Arm NN Driver Buffer Overflow Vulnerability

CVE-2023-26085 Overview

CVE-2023-26085 is an out-of-bounds read and write vulnerability discovered in the Arm NN Android Neural Networks Driver. The flaw stems from an improper length check when handling shared memory operations, which can allow an attacker with local access to read or write memory outside the intended bounds. This type of memory corruption vulnerability can potentially lead to information disclosure, code execution, or system instability on affected Android devices utilizing Arm's neural network acceleration capabilities.

Critical Impact

Local attackers with low privileges can exploit this vulnerability to achieve high impact on confidentiality, integrity, and availability of the affected system.

Affected Products

• Arm NN Android Neural Networks Driver versions prior to 23.02
• Android devices utilizing Arm NN for neural network inference acceleration
• Systems integrating Arm NN Android-NN-Driver for machine learning workloads

Discovery Timeline

• 2023-06-29 - CVE-2023-26085 published to NVD
• 2024-11-21 - Last updated in NVD database

Technical Details for CVE-2023-26085

Vulnerability Analysis

This vulnerability is classified as CWE-125 (Out-of-bounds Read), though the impact extends to both out-of-bounds read and write conditions. The flaw occurs within the shared memory handling routines of the Arm NN Android Neural Networks Driver. When the driver processes shared memory allocations for neural network operations, it fails to properly validate the length parameters, creating an opportunity for memory access beyond allocated boundaries.

The local attack vector means an attacker would need to execute code on the target device, but the low attack complexity and minimal privilege requirements make this vulnerability accessible to malicious applications. Successful exploitation could enable an attacker to read sensitive data from adjacent memory regions, corrupt memory to cause denial of service, or potentially achieve arbitrary code execution depending on the memory layout and exploitation technique employed.

Root Cause

The root cause of CVE-2023-26085 is an improper length check when validating shared memory regions in the Arm NN Android-NN-Driver. The driver fails to adequately verify that memory access operations stay within the bounds of allocated shared memory buffers. This boundary validation failure allows read and write operations to access memory outside the intended allocation, leading to classic memory corruption conditions.

Attack Vector

The attack vector for this vulnerability is local, requiring an attacker to have the ability to execute code on the target Android device. A malicious application could craft specific inputs to the Android Neural Networks API that trigger the improper length check condition. By manipulating shared memory parameters passed to the vulnerable driver, an attacker can cause the driver to read from or write to memory locations outside the expected buffer boundaries.

The vulnerability mechanism involves crafting neural network operations that utilize shared memory with malformed length parameters. When the driver processes these requests without proper validation, it accesses memory beyond the allocated regions. This can be leveraged to leak sensitive information through out-of-bounds reads or to corrupt memory state through out-of-bounds writes.

Detection Methods for CVE-2023-26085

Indicators of Compromise

• Unexpected crashes or instability in applications utilizing neural network acceleration
• Memory corruption errors or segmentation faults in system logs related to the NN driver
• Anomalous behavior in Android Neural Networks HAL service processes
• Suspicious applications making excessive or unusual calls to the Neural Networks API

Detection Strategies

• Monitor system logs for memory access violations or crashes originating from android.hardware.neuralnetworks services
• Implement runtime memory protection mechanisms to detect out-of-bounds memory access attempts
• Deploy application-level monitoring to identify unusual Neural Networks API call patterns
• Utilize SentinelOne Singularity platform for behavioral detection of memory corruption exploit attempts

Monitoring Recommendations

• Enable verbose logging for Android HAL services to capture detailed execution information
• Implement memory sanitizers in development and testing environments to identify boundary violations
• Monitor for privilege escalation attempts following anomalous NN driver behavior
• Track system stability metrics for devices running applications with neural network workloads

How to Mitigate CVE-2023-26085

Immediate Actions Required

• Upgrade Arm NN Android-NN-Driver to version 23.02 or later immediately
• Audit deployed Android devices for vulnerable driver versions
• Restrict installation of untrusted applications that may attempt to exploit this vulnerability
• Consider disabling or limiting access to neural network acceleration features on critical systems until patching is complete

Patch Information

Arm has addressed this vulnerability in version 23.02 of the NN Android Neural Networks Driver. The fix implements proper length validation for shared memory operations, preventing out-of-bounds read and write conditions. Organizations should obtain the patched driver from the official GitHub Release v23.02 or through their device manufacturer's update channels. Additional security information is available at the ARM Security Center.

Workarounds

• Limit installation of applications to trusted sources only to reduce attack surface
• Implement application sandboxing and permission restrictions to limit potential exploitation impact
• Monitor and restrict access to Neural Networks API for non-essential applications
• Deploy endpoint protection solutions capable of detecting memory corruption attempts

# Verify current driver version on Android device
adb shell dumpsys neuralnetworks | grep -i version

# Check for available system updates
adb shell pm list packages | grep -i neural