CVE-2024-25989 in Android
Summary
by MITRE • 03/11/2024
In gpu_slc_liveness_update of pixel_gpu_slc.c, there is a possible out of bounds read due to a missing bounds check. This could lead to local information disclosure with no additional execution privileges needed. User interaction is not needed for exploitation.
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Analysis
by VulDB Data Team • 11/08/2024
The vulnerability identified as CVE-2024-25989 resides within the pixel_gpu_slc.c source file, specifically in the gpu_slc_liveness_update function where a critical out-of-bounds read condition exists. This flaw represents a classic buffer overread vulnerability that occurs when the code fails to validate array indices before accessing memory locations. The missing bounds check creates a scenario where the function may attempt to read data from memory addresses beyond the allocated buffer boundaries, potentially exposing sensitive information stored in adjacent memory locations.
The technical implementation of this vulnerability demonstrates a failure in input validation and memory management practices that aligns with CWE-129, which specifically addresses insufficient checking of the length of input data. The flaw operates at the kernel level within GPU subsystems, making it particularly concerning as it can be exploited to extract information that may include kernel memory contents, cryptographic keys, or other sensitive data structures. The vulnerability does not require any special privileges beyond normal user access, and no user interaction is necessary for exploitation, making it highly dangerous in environments where untrusted users have access to GPU resources.
From an operational perspective, this vulnerability creates a significant risk for systems that rely on GPU processing for sensitive operations, particularly in enterprise environments where GPU acceleration is common for machine learning, graphics processing, and cryptographic operations. The local information disclosure threat means that an attacker with minimal privileges could potentially extract confidential information from the system's memory, which could then be used to facilitate further attacks or compromise system integrity. The lack of privilege escalation requirements and user interaction needs makes this vulnerability particularly attractive to attackers seeking to gather intelligence without raising immediate detection flags.
The impact of this vulnerability extends beyond simple information disclosure as it can provide attackers with insights into system memory layouts, potentially enabling more sophisticated attacks such as heap spraying or memory corruption exploits. The ATT&CK framework categorizes this type of vulnerability under T1005 - Data from Local System, where adversaries can collect sensitive information from compromised systems. Additionally, this vulnerability could serve as a stepping stone for privilege escalation attacks or be combined with other flaws to create more severe security breaches. Organizations should prioritize patching this vulnerability as it represents a fundamental security flaw in GPU memory management that can be exploited without requiring elevated privileges or user interaction.
Mitigation strategies should include immediate deployment of security patches provided by device manufacturers and kernel maintainers, along with monitoring for unusual memory access patterns that might indicate exploitation attempts. System administrators should also consider implementing memory protection mechanisms such as stack canaries, address space layout randomization, and kernel memory protection features to reduce the effectiveness of potential exploitation attempts. Regular security assessments of GPU subsystems and kernel modules should be conducted to identify similar vulnerabilities that may exist in other components of the system architecture.