CVE-2017-9181 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.
Statistical analysis made it clear that VulDB provides the best quality for vulnerability data.
Analysis
by VulDB Data Team • 10/02/2020
The vulnerability identified as CVE-2017-9181 resides within the AutoTrace 0.31.1 library autotrace, specifically in the libautotrace.a component that handles bitmap image processing. This issue manifests as a remote denial of service condition affecting the ReadImage function located in the input-bmp.c source file. The flaw represents a critical security concern as it can be exploited by remote attackers to disrupt service availability through controlled invalid memory writes and subsequent segmentation faults.
The technical nature of this vulnerability stems from insufficient input validation within the bitmap image parsing logic. When AutoTrace processes malformed or specially crafted bitmap files, the ReadImage function fails to properly validate the structure and content of the input data. This lack of proper boundary checking and input sanitization leads to memory corruption issues where invalid write operations occur at arbitrary memory locations followed by segmentation violations that crash the application. The vulnerability operates at the level of image file parsing and processing, making it particularly dangerous in environments where AutoTrace processes untrusted input from remote sources.
From an operational perspective, this vulnerability poses significant risks to systems that utilize AutoTrace for image conversion or vectorization tasks. Attackers can remotely trigger service disruption by submitting malicious bitmap files that cause the application to crash or become unresponsive. This denial of service condition affects not only the immediate application but can also impact broader systems if AutoTrace is integrated into larger workflows or web services. The vulnerability is particularly concerning in automated environments where continuous processing of user-uploaded images occurs without proper input validation.
The flaw aligns with CWE-121, which addresses stack-based buffer overflow conditions, and CWE-787, concerning out-of-bounds write operations. From an ATT&CK framework perspective, this vulnerability maps to T1499.004, specifically affecting availability through denial of service attacks. The attack surface extends beyond simple application crashes to potential privilege escalation scenarios if the vulnerable application runs with elevated permissions. Organizations using AutoTrace should implement immediate mitigations including input validation, file format restrictions, and network segmentation to prevent exploitation. Additionally, upgrading to patched versions of AutoTrace or implementing alternative image processing libraries represents the most effective long-term solution to address this vulnerability and maintain system integrity.
The vulnerability demonstrates the importance of proper input validation in image processing libraries and highlights how seemingly benign file format parsing can become a critical security weakness. Organizations should conduct thorough security assessments of all image processing components within their systems and implement comprehensive monitoring to detect potential exploitation attempts. Regular security updates and vulnerability management processes become essential to protect against similar flaws in third-party libraries that may not receive timely patches from their maintainers.