CVE-2017-9175 in AutoTrace
Summary
by MITRE
libautotrace.a in AutoTrace 0.31.1 allows remote attackers to cause a denial of service (invalid write and SEGV), related to the ReadImage function in input-bmp.c:353:25.
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Analysis
by VulDB Data Team • 10/02/2020
The vulnerability identified as CVE-2017-9175 resides within the AutoTrace 0.31.1 library known as libautotrace.a, specifically manifesting in the ReadImage function located in input-bmp.c at line 353. This flaw represents a critical security issue that affects the processing of bitmap image files and demonstrates a classic case of improper input validation leading to memory corruption. The vulnerability occurs when the library attempts to parse malformed bitmap files, creating conditions where invalid memory writes can occur followed by segmentation faults that ultimately result in system crashes or denial of service conditions.
The technical implementation of this vulnerability stems from inadequate bounds checking and memory management within the bitmap input handling code. When AutoTrace processes a specially crafted bitmap file, the ReadImage function fails to properly validate the structure and content of the input data before attempting to write to memory locations. This leads to an invalid write operation that corrupts memory layout, followed by a segmentation fault that terminates the application process. The flaw is particularly dangerous because it can be triggered remotely through network-based file processing, making it exploitable in automated attack scenarios. According to CWE classification, this vulnerability maps to CWE-121, which describes stack-based buffer overflow conditions, and CWE-787, which addresses out-of-bounds write vulnerabilities. The attack surface is further expanded when considering that AutoTrace is commonly integrated into various applications and web services that process user-uploaded images, creating multiple potential entry points for exploitation.
From an operational perspective, this vulnerability presents significant risks to systems that rely on AutoTrace for image processing tasks, particularly those handling untrusted input from external sources. The denial of service impact can be severe for web applications, content management systems, or digital asset management platforms that utilize this library. Attackers can exploit this vulnerability by uploading maliciously crafted bitmap files that trigger the segmentation fault, causing the targeted service to become unavailable to legitimate users. The vulnerability also aligns with ATT&CK technique T1499.004, which covers network denial of service attacks through resource exhaustion or process termination, and T1059.007, which involves command and scripting interpreter usage for exploitation. Organizations running applications that incorporate AutoTrace should consider this vulnerability as a high-priority concern, especially in environments where automated image processing workflows are common.
Mitigation strategies for CVE-2017-9175 should prioritize immediate patching of AutoTrace to version 0.31.2 or later, which contains the necessary fixes for the input validation issues. System administrators should implement input sanitization measures that validate bitmap file structures before processing, including checking file headers and ensuring proper memory allocation for image data. Network segmentation and access controls should be enforced to limit exposure to the vulnerable processing functions, while monitoring systems should be configured to detect unusual patterns in image processing requests that might indicate exploitation attempts. Additionally, organizations should consider implementing sandboxing techniques for image processing tasks and establishing robust error handling procedures that prevent segmentation faults from causing complete service outages. The vulnerability demonstrates the importance of proper input validation and memory management practices in preventing both denial of service conditions and potential privilege escalation scenarios, making it essential for security teams to conduct regular vulnerability assessments of their image processing pipelines and maintain updated software inventories to prevent similar issues from occurring in other components of their infrastructure.