CVE-2018-10187 in radare2
Summary
by MITRE
In radare2 2.5.0, there is a heap-based buffer over-read in the dalvik_op function (libr/anal/p/anal_dalvik.c). Remote attackers could leverage this vulnerability to cause a denial of service via a crafted DEX file. Note that this issue is different from CVE-2018-8809, which was patched earlier.
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Analysis
by VulDB Data Team • 02/28/2023
The vulnerability identified as CVE-2018-10187 represents a critical heap-based buffer over-read condition within the radare2 reverse engineering framework version 2.5.0. This flaw specifically affects the dalvik_op function located in the file libr/anal/p/anal_dalvik.c, which is responsible for analyzing Dalvik bytecode commonly found in android application packages. The issue arises during the static analysis of DEX files where the application fails to properly validate input boundaries before accessing memory regions, creating a scenario where arbitrary memory locations can be read beyond the intended buffer limits. The vulnerability's classification as heap-based indicates that the over-read occurs within dynamically allocated memory segments managed by the heap allocator, making it particularly dangerous as it can expose sensitive data or trigger system instability.
The technical exploitation of this vulnerability requires a remote attacker to craft a malicious DEX file that specifically triggers the buffer over-read condition when radare2 attempts to analyze the file. When the dalvik_op function processes malformed or specially constructed bytecode instructions, it reads data from memory locations that extend beyond the allocated buffer boundaries, potentially accessing uninitialized memory, stack data, or other process memory regions. This behavior constitutes a direct violation of memory safety principles and can lead to unpredictable program behavior, including crashes, data corruption, or in some cases, information disclosure. The vulnerability demonstrates a classic lack of proper bounds checking in the analysis engine, which is a common weakness in binary analysis tools that must process untrusted input formats.
The operational impact of CVE-2018-10187 extends beyond simple denial of service, as it can potentially expose sensitive information contained in the heap memory of the affected process. When an attacker successfully triggers this vulnerability through a crafted DEX file, the over-read may inadvertently reveal memory contents that could include cryptographic keys, user credentials, or other confidential data stored in adjacent memory regions. This makes the vulnerability particularly concerning for security analysis tools that process untrusted files, as it could be leveraged to extract sensitive information from the analysis environment itself. The vulnerability's remote exploitability means that attackers can trigger the condition without requiring local access to the system, making it a significant threat to automated analysis systems or tools that process files from untrusted sources.
Mitigation strategies for CVE-2018-10187 should prioritize immediate patching of affected radare2 installations to version 2.5.1 or later, which contains the necessary fixes for the buffer over-read condition. Organizations should also implement defensive measures such as input validation and sandboxing when processing potentially malicious DEX files, ensuring that analysis environments are isolated from critical systems. The vulnerability aligns with CWE-125, which describes out-of-bounds read conditions in software implementations, and represents a clear violation of the principle of least privilege in memory management. From an ATT&CK framework perspective, this vulnerability could be categorized under initial access and execution techniques where adversaries leverage analysis tools to gain information about target systems, potentially enabling more sophisticated attacks that exploit the information disclosure aspect of the flaw. Regular security assessments of reverse engineering tools and their input handling mechanisms should be conducted to identify similar vulnerabilities that could compromise system integrity and data confidentiality.