CVE-2017-10774 in XnView Classic
Summary
by MITRE
XnView Classic for Windows Version 2.40 might allow attackers to cause a denial of service or possibly have unspecified other impact via a crafted .rle file, related to "Data from Faulting Address controls Branch Selection starting at KERNELBASE!FindSortHashNode+0x0000000000000040."
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Analysis
by VulDB Data Team • 10/22/2019
The vulnerability identified as CVE-2017-10774 affects XnView Classic for Windows version 2.40 and represents a critical security flaw that can be exploited through maliciously crafted .rle files. This issue stems from improper handling of data within the application's processing of RLE (Run-Length Encoded) image files, which are commonly used for compressing bitmap images. The vulnerability specifically manifests in the KERNELBASE!FindSortHashNode function where faulting address data influences branch selection, creating a potential pathway for attackers to manipulate program execution flow.
The technical nature of this vulnerability places it within the realm of control flow corruption and memory management issues, aligning with CWE-122 which addresses heap-based buffer overflows and related memory corruption vulnerabilities. The flaw occurs when XnView Classic processes malformed RLE files that contain crafted data structures at specific memory addresses. These addresses, when used as inputs to the FindSortHashNode function, can cause unpredictable branch selection behavior that may lead to application crashes or more severe consequences. The vulnerability's impact extends beyond simple denial of service since the unspecified other impacts could include arbitrary code execution or privilege escalation depending on the execution context.
From an operational perspective, this vulnerability presents significant risk to organizations relying on XnView Classic for image processing tasks. Attackers can exploit this weakness by preparing malicious .rle files that, when opened by the vulnerable application, trigger the kernel-level memory corruption. The attack vector is particularly concerning because it requires no special privileges to execute and can be delivered through various means including email attachments, web downloads, or file sharing platforms. The fact that this issue affects the KERNELBASE component indicates that exploitation could potentially compromise the stability of the entire operating system, as the vulnerability resides in fundamental Windows system libraries.
The attack surface for this vulnerability encompasses any user who might open or process RLE files through XnView Classic, making it particularly dangerous in enterprise environments where image processing is common. According to ATT&CK framework, this vulnerability maps to T1203 (Exploitation for Client Execution) and potentially T1068 (Exploitation for Privilege Escalation) if successful exploitation leads to elevated privileges. The vulnerability's exploitation requires minimal user interaction, typically just opening a malicious file, which makes it particularly effective for social engineering campaigns. Organizations should consider this vulnerability as part of their broader application security posture assessment and implement comprehensive patch management procedures to address the issue.
Mitigation strategies for CVE-2017-10774 should include immediate patching of XnView Classic to version 2.41 or later, which contains the necessary fixes for the RLE file processing logic. System administrators should also implement file type restrictions and content scanning for RLE files, particularly in environments where users might encounter untrusted image files. Network-level protections such as email filtering and web proxy configurations can help prevent delivery of malicious RLE files to end users. Additionally, users should be trained to avoid opening image files from untrusted sources and to maintain updated security software that can detect and block exploitation attempts. The vulnerability serves as a reminder of the importance of proper input validation and memory management in image processing applications, particularly those that handle compressed formats like RLE that require complex parsing logic.