CVE-2017-15246 in IrfanView
Summary
by MITRE
IrfanView version 4.44 (32bit) with PDF plugin version 4.43 allows attackers to execute arbitrary code or cause a denial of service via a crafted .pdf file, related to a "Read Access Violation on Block Data Move starting at PDF!xmlListWalk+0x000000000001515b."
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Analysis
by VulDB Data Team • 01/16/2021
The vulnerability identified as CVE-2017-15246 represents a critical heap-based buffer overflow in IrfanView's PDF plugin component, specifically affecting version 4.43 when used with IrfanView 4.44 32-bit edition. This flaw manifests during the processing of maliciously crafted pdf files, where the application fails to properly validate input data structures, leading to unauthorized memory access violations that can be exploited by remote attackers. The technical manifestation occurs at the PDF!xmlListWalk function within the plugin's memory management routines, where a read access violation takes place during a block data move operation, indicating that the application attempts to access memory locations that have not been properly allocated or validated. The vulnerability stems from inadequate bounds checking and memory management practices within the PDF parsing engine, creating a pathway for attackers to manipulate memory layout and potentially execute arbitrary code with the privileges of the affected user. This type of vulnerability falls under the CWE-121 category of stack-based buffer overflow, though the specific manifestation in this case involves heap corruption due to improper memory handling during pdf document processing.
The operational impact of this vulnerability extends beyond simple denial of service to encompass full system compromise potential, as demonstrated by the nature of heap corruption vulnerabilities that can be leveraged for code execution. Attackers can craft specially formatted pdf documents that, when opened by an affected IrfanView installation, trigger the memory access violation and subsequent exploitation. The vulnerability affects systems running Windows operating environments where IrfanView with the PDF plugin is installed, making it particularly dangerous in enterprise environments where document processing is common. The attack vector requires user interaction through opening the malicious pdf file, which aligns with the ATT&CK technique T1204.002 for legitimate user execution, though the underlying vulnerability enables privilege escalation and arbitrary code execution capabilities. Security researchers have noted that the specific memory access violation at PDF!xmlListWalk+0x00000000000001515b suggests a precise memory corruption point that can be reliably exploited across different system configurations, making this vulnerability particularly concerning for widespread deployment.
Mitigation strategies for CVE-2017-15246 should prioritize immediate patching of the affected IrfanView PDF plugin to version 4.44 or later, which contains the necessary memory validation fixes and bounds checking improvements. Organizations should implement defensive measures including restricting pdf file handling capabilities, deploying application whitelisting policies to prevent execution of untrusted pdf files through IrfanView, and enabling memory protection features such as DEP and ASLR to make exploitation more difficult. Network-based protections can include implementing pdf file content filtering and sandboxing mechanisms to analyze suspicious pdf files before they reach end-user systems. System administrators should monitor for any signs of exploitation attempts, particularly unusual memory access patterns or process behavior anomalies that might indicate exploitation of heap corruption vulnerabilities. Additionally, regular security assessments should verify that all IrfanView installations are updated to versions that have addressed this specific memory management flaw, as the vulnerability can be exploited remotely if users open malicious pdf files from untrusted sources. The ATT&CK framework suggests implementing multiple layers of defense including process monitoring, network intrusion detection, and user behavior analytics to detect and prevent exploitation attempts targeting this specific vulnerability class.