CVE-2017-9195 in AutoTrace
Summary
by MITRE
libautotrace.a in AutoTrace 0.31.1 has a heap-based buffer over-read in the ReadImage function in input-tga.c:620:27.
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Analysis
by VulDB Data Team • 10/02/2020
The vulnerability identified as CVE-2017-9195 resides within the AutoTrace library autotrace.a version 0.31.1, specifically within the ReadImage function located in the input-tga.c file at line 620 column 27. This represents a heap-based buffer over-read condition that occurs during the processing of Targa image files, making it particularly concerning for applications that utilize AutoTrace for image format conversion and vectorization operations. The flaw manifests when the library attempts to read beyond the allocated memory boundaries while parsing TGA file headers and pixel data structures.
The technical implementation of this vulnerability stems from inadequate bounds checking within the input-tga.c module where the ReadImage function processes TGA image data. At the specific memory location indicated by the stack trace, the code performs a read operation that exceeds the allocated buffer size, potentially accessing memory that has not been properly initialized or is otherwise outside the expected data boundaries. This over-read condition creates a scenario where arbitrary memory content may be read and subsequently processed, leading to unpredictable behavior and potential information disclosure. The vulnerability aligns with CWE-125, which describes out-of-bounds read conditions that can result in information exposure or system instability.
The operational impact of this vulnerability extends beyond simple memory access violations, as it presents potential exploitation vectors for attackers seeking to compromise systems that utilize AutoTrace in their workflows. When an application processes maliciously crafted TGA files through AutoTrace, the heap-based over-read can lead to information leakage from adjacent memory regions, potentially exposing sensitive data such as stack contents, heap metadata, or other application state information. This information disclosure can be particularly dangerous in environments where AutoTrace is used for automated image processing, as it may enable attackers to gather intelligence about system memory layouts or application state for further exploitation attempts.
The vulnerability demonstrates a classic example of insufficient input validation that violates fundamental security principles outlined in various security frameworks including the ATT&CK framework's concept of privilege escalation through memory corruption techniques. Systems that rely on AutoTrace for image processing, particularly those handling untrusted input from web applications, file upload systems, or automated processing pipelines, become vulnerable to this type of memory corruption attack. The exploitability of this condition is enhanced when AutoTrace is integrated into larger applications or services that do not properly sanitize input file formats before passing them to the vulnerable library function.
Mitigation strategies for CVE-2017-9195 should prioritize immediate patching of affected AutoTrace installations to version 0.31.2 or later, which includes the necessary bounds checking fixes. Organizations should implement input validation measures that verify TGA file structures before processing, including checking file headers against expected formats and validating data sizes before memory allocation. Additionally, deploying memory safety tools such as address sanitizers, heap profilers, and static analysis tools can help identify similar vulnerabilities in other components of the software stack. Network segmentation and application whitelisting can provide additional defense-in-depth measures to limit the potential impact of exploitation attempts, while regular security assessments should be conducted to identify other potential buffer over-read conditions in similar image processing libraries.