CVE-2019-2143 in Android
Summary
by MITRE
In libxaac, there is a possible out of bounds read due to a missing bounds check. This could lead to information disclosure with no additional execution privileges needed. User interaction is needed for exploitation. Product: AndroidVersions: Android-10Android ID: A-114746174
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Analysis
by VulDB Data Team • 09/11/2020
The vulnerability identified as CVE-2019-2143 affects the libxaac library component within Android systems, specifically manifesting in Android 10 deployments. This issue represents a classic out-of-bounds read condition that stems from inadequate input validation mechanisms within the audio decoding framework. The flaw exists within the AAC (Advanced Audio Coding) audio processing subsystem where the library fails to properly validate array indices or buffer boundaries before accessing memory locations. Such missing bounds checks create opportunities for attackers to manipulate input audio data in ways that cause the application to read memory beyond intended boundaries, potentially exposing sensitive information stored in adjacent memory regions.
The technical exploitation of this vulnerability requires user interaction, meaning an attacker must convince a victim to process specifically crafted malicious audio content through the affected system. This interaction typically occurs when users play or process audio files that have been deliberately constructed to trigger the out-of-bounds read condition. The vulnerability's impact is classified as information disclosure rather than arbitrary code execution, which means that while attackers cannot directly execute malicious code, they can potentially extract confidential data from memory locations. This type of vulnerability falls under CWE-129, which specifically addresses insufficient validation of length of input buffers, and more broadly aligns with CWE-125, representing out-of-bounds read conditions. The ATT&CK framework categorizes this as a privilege escalation technique through software exploitation, specifically under the T1068 technique of Exploitation for Privilege Escalation.
From an operational perspective, this vulnerability poses significant risks to Android device security as it can potentially expose sensitive information stored in memory, including cryptographic keys, user credentials, or application data that might be present in adjacent memory locations. The fact that no additional execution privileges are required for exploitation makes this particularly concerning, as it means that an attacker can leverage this vulnerability through standard user-level interactions without needing elevated system permissions. The Android security model relies heavily on proper input validation and bounds checking within system libraries, and this vulnerability demonstrates how a flaw in a widely-used audio processing component can create potential information leakage pathways. The Android ID A-114746174 indicates this was properly tracked within Google's internal vulnerability management system, highlighting the severity and proper classification of the issue.
Mitigation strategies for CVE-2019-2143 primarily focus on updating affected Android systems to versions that contain patches addressing the bounds checking deficiencies in libxaac. System administrators should ensure that all Android devices are updated to the latest security patches released by Google, particularly those addressing the specific audio processing library vulnerabilities. Additionally, organizations should implement network-level controls to prevent the download and execution of untrusted audio content, particularly in environments where security is paramount. The vulnerability serves as a reminder of the importance of proper input validation in multimedia processing libraries, where attackers can leverage seemingly benign file formats to extract sensitive information from memory. Security teams should also consider implementing runtime monitoring to detect anomalous memory access patterns that might indicate exploitation attempts, as this type of vulnerability can be difficult to detect through traditional static analysis methods due to its reliance on specific user interaction scenarios.