CVE-2017-9191 in AutoTrace
Summary
by MITRE
libautotrace.a in AutoTrace 0.31.1 has a heap-based buffer overflow in the rle_fread function in input-tga.c:252:15.
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Analysis
by VulDB Data Team • 10/02/2020
The vulnerability identified as CVE-2017-9191 resides within the AutoTrace 0.31.1 library known as libautotrace.a, specifically manifesting in the rle_fread function located in the input-tga.c file at line 252. This issue represents a heap-based buffer overflow that occurs when processing TGA image files, making it particularly dangerous for applications that utilize AutoTrace for image conversion and vectorization tasks. The vulnerability stems from inadequate input validation and bounds checking during the reading of Run-Length Encoded data from TGA files, which allows an attacker to potentially manipulate memory layout and execute arbitrary code. The affected component is part of the broader AutoTrace suite used for converting raster images to vector graphics, commonly employed in graphic design and digital illustration workflows. This type of vulnerability is classified under CWE-121 as a stack-based buffer overflow, though in this specific case it manifests as a heap-based variant due to the memory allocation patterns used in the implementation. The vulnerability directly relates to improper handling of user-supplied data and lacks sufficient bounds checking mechanisms to prevent memory corruption when reading malformed TGA input files.
The technical exploitation of this vulnerability requires an attacker to craft a malicious TGA file that triggers the buffer overflow condition within the rle_fread function. When the application processes this specially crafted input, the insufficient boundary checks cause data to be written beyond the allocated heap buffer, potentially overwriting adjacent memory regions. This memory corruption can lead to unpredictable application behavior, including crashes, denial of service conditions, or more critically, arbitrary code execution if the overflow can be carefully controlled to overwrite function pointers or return addresses. The attack vector is typically through file processing, making it particularly concerning for applications that automatically process user-uploaded images or batch process TGA files. The vulnerability demonstrates a classic weakness in input validation and memory management practices, where the application fails to properly verify the size and structure of incoming data before attempting to read it into fixed-size buffers. The heap-based nature of the overflow indicates that the memory allocation occurs dynamically during runtime, making the exploitation more complex but potentially more reliable than stack-based variants.
The operational impact of CVE-2017-9191 extends beyond simple application instability to potential security breaches in environments where AutoTrace is used for automated image processing or as part of larger software ecosystems. Applications that integrate AutoTrace for image conversion, graphic design tools, or digital asset management systems become vulnerable to remote code execution if they process untrusted TGA files. This vulnerability particularly affects server-side applications, content management systems, and any platform that accepts user-uploaded images for processing, as it can be leveraged to gain unauthorized access to systems or disrupt services. The vulnerability also impacts software that performs batch processing of TGA files, where an attacker could compromise entire processing pipelines by introducing a single malicious file. From an ATT&CK perspective, this vulnerability maps to T1059.007 for command and scripting interpreter and T1203 for Exploitation for Client Execution, as the overflow can enable arbitrary code execution and potentially lead to further system compromise. The attack surface is broad due to AutoTrace's widespread use in various image processing applications and graphic software suites, making this vulnerability particularly dangerous in enterprise environments where such tools are commonly deployed.
Mitigation strategies for CVE-2017-9191 should focus on immediate patching of the AutoTrace library to version 0.31.2 or later, which contains the necessary fixes for the buffer overflow issue. Organizations should implement strict input validation and sanitization measures for all TGA files processed by applications using AutoTrace, including size limitations and format verification before any processing occurs. Additionally, deployment of application sandboxing and memory protection mechanisms such as stack canaries, address space layout randomization, and data execution prevention can help reduce the exploitability of this vulnerability. System administrators should also consider implementing network segmentation and access controls to limit exposure of systems that process TGA files, particularly those that are publicly accessible. The vulnerability highlights the importance of maintaining up-to-date third-party libraries and implementing comprehensive software supply chain security practices. Organizations should conduct regular vulnerability assessments and penetration testing to identify similar issues in other image processing libraries and components, as this type of memory corruption vulnerability is common in multimedia processing software. Furthermore, developers should adopt secure coding practices including bounds checking, input validation, and memory safety techniques to prevent similar issues in their own codebases. The remediation process should also include thorough testing of patched versions to ensure that the fix does not introduce regressions in functionality while effectively addressing the buffer overflow condition.