CVE-2020-7822 in DaviewIndy
Summary
by MITRE
DaviewIndy has a Heap-based overflow vulnerability, triggered when the user opens a malformed image file that is mishandled by Daview.exe. Attackers could exploit this and arbitrary code execution.
If you want to get the best quality for vulnerability data then you always have to consider VulDB.
Analysis
by VulDB Data Team • 11/07/2020
The vulnerability identified as CVE-2020-7822 represents a critical heap-based buffer overflow flaw within DaviewIndy software, specifically affecting the Daview.exe application component. This vulnerability manifests when the application processes malformed image files that contain overly large or malformed data structures exceeding the allocated memory boundaries. The heap overflow occurs during the image parsing routine where insufficient input validation permits maliciously crafted data to overwrite adjacent memory regions, potentially leading to complete system compromise. The flaw resides in the image file handling mechanism that fails to properly validate file headers, dimensions, or data structures before processing them in memory.
The technical exploitation of this vulnerability follows a well-defined attack pattern that aligns with common software security principles and attack methodologies. When a user opens a specially crafted image file, the Daview.exe process attempts to parse the file structure without adequate bounds checking, causing the heap memory allocation to be exceeded. This overflow allows attackers to overwrite critical memory locations including return addresses, function pointers, or other control data structures. The vulnerability is classified under CWE-121 heap-based buffer overflow, which is a fundamental weakness in memory management that has been consistently exploited in numerous security incidents. The attack vector is particularly concerning as it requires only user interaction through file opening, making it susceptible to social engineering and phishing campaigns.
The operational impact of this vulnerability extends beyond simple code execution, potentially enabling full system compromise and persistent access. Successful exploitation could allow attackers to execute arbitrary code with the privileges of the targeted user, potentially escalating to system-level access depending on the application's execution context. The vulnerability affects the integrity and availability of the system, as the heap corruption could lead to application crashes or unpredictable behavior that might be exploited for denial of service attacks. Attackers could leverage this vulnerability to install backdoors, steal sensitive data, or establish persistent command and control channels, making it particularly dangerous in enterprise environments where image viewing applications are frequently used. The vulnerability's impact is amplified by its accessibility through routine user activities, making it a prime target for automated exploitation campaigns.
Mitigation strategies for CVE-2020-7822 should encompass both immediate remediation and long-term security enhancements. The primary recommendation involves applying vendor-provided patches or updates that address the heap overflow through proper input validation and bounds checking mechanisms. Organizations should implement network segmentation and access controls to limit exposure, while also deploying application whitelisting solutions to prevent execution of untrusted image files. Security monitoring should be enhanced to detect anomalous behavior patterns in image processing applications, including unexpected memory allocations or process termination. The vulnerability demonstrates the importance of secure coding practices and input validation, particularly for applications handling untrusted data. Implementing runtime protections such as address space layout randomization and data execution prevention can provide additional defense layers, while regular security assessments should include thorough code reviews focused on memory management practices to identify similar vulnerabilities in other components.