CVE-2021-30311 in Snapdragon Auto
Summary
by MITRE • 01/13/2022
Possible heap overflow due to lack of index validation before allocating and writing to heap buffer in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Industrial IOT, Snapdragon Mobile
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Analysis
by VulDB Data Team • 01/16/2022
This heap overflow vulnerability resides within Qualcomm Snapdragon automotive and mobile platform components, specifically affecting systems that utilize Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Industrial IOT, and Snapdragon Mobile architectures. The flaw stems from inadequate input validation mechanisms that fail to properly verify index boundaries before proceeding with heap buffer allocation and subsequent data writing operations. The vulnerability manifests when the system processes user-supplied or malformed input that contains an out-of-bounds index value, leading to unauthorized memory access patterns that can corrupt adjacent heap memory regions.
The technical implementation of this vulnerability involves a classic buffer management error where the system allocates heap memory based on an index value without proper validation of whether that index falls within acceptable operational parameters. When the index exceeds the allocated buffer boundaries, subsequent write operations can overwrite adjacent memory locations, potentially corrupting critical data structures, function pointers, or control flow information. This type of vulnerability aligns with CWE-121, which describes stack-based buffer overflow conditions, though in this case the heap memory corruption represents a more complex exploitation vector due to the dynamic nature of heap allocation patterns and memory layout.
The operational impact of this vulnerability spans across multiple automotive and industrial domains where Snapdragon platforms are deployed, including autonomous vehicle systems, industrial IoT devices, and mobile communication infrastructure. An attacker could potentially exploit this condition to execute arbitrary code within the affected system's memory space, leading to complete system compromise. The vulnerability's presence in automotive applications raises particular concerns regarding vehicle safety systems and autonomous driving capabilities, where memory corruption could result in critical system failures or unauthorized control of vehicle functions. The attack surface extends to any application that utilizes Snapdragon platform components and processes external input through memory allocation functions.
Mitigation strategies should focus on implementing comprehensive input validation mechanisms that verify all index values against known boundary conditions before any memory allocation occurs. System architects should deploy bounds checking routines and utilize memory-safe programming practices to prevent unauthorized memory access patterns. The implementation of address space layout randomization and stack canaries can provide additional layers of protection against exploitation attempts. Organizations should also maintain rigorous firmware update protocols to ensure that all affected Snapdragon platform deployments receive timely security patches. This vulnerability demonstrates the critical importance of memory safety practices in embedded systems, particularly those operating in safety-critical environments where the consequences of memory corruption can extend beyond traditional software security concerns into physical safety and operational reliability domains.