CVE-2019-20056 in stb Image Loader
Summary
by MITRE
stb_image.h (aka the stb image loader) 2.23, as used in libsixel and other products, has an assertion failure in stbi__shiftsigned.
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Analysis
by VulDB Data Team • 03/18/2024
The vulnerability identified as CVE-2019-20056 affects the stb_image.h library, commonly known as the stb image loader, version 2.23. This library is widely utilized in various software products including libsixel and numerous other applications that require image processing capabilities. The stb image loader is a popular single-header library for loading images in various formats, making it a critical component in many software ecosystems. The vulnerability manifests as an assertion failure within the stbi__shiftsigned function, which is part of the internal processing logic for handling image data. This assertion failure occurs during the parsing of image files, specifically when the library encounters certain malformed or crafted input data that triggers an unexpected condition in the code execution flow.
The technical flaw resides in the stbi__shiftsigned function where an assertion check fails when processing specific image data patterns. This assertion failure represents a denial of service vulnerability that can cause the application using the stb image loader to crash or terminate unexpectedly. The vulnerability is particularly concerning because it occurs during the image loading process, which means any application that utilizes this library for image parsing could be susceptible to this crash condition. The assertion failure typically results in a program crash or abnormal termination when the library attempts to process malformed image files, potentially allowing attackers to cause service disruption through crafted image inputs. This behavior aligns with CWE-617, which describes reachable assertions that can be triggered by external inputs, and represents a classic example of a software fault that can lead to denial of service conditions.
The operational impact of this vulnerability extends beyond simple application crashes, as it can affect numerous software products that depend on the stb image loader for image processing capabilities. Applications that process user-uploaded images or external image data sources become particularly vulnerable to this issue, as attackers could craft malicious image files designed to trigger the assertion failure. The vulnerability affects not only individual applications but also entire software ecosystems that rely on this widely-used library. In environments where image processing is a core functionality, such as web applications, content management systems, or multimedia processing tools, this vulnerability could be exploited to disrupt services and cause significant operational downtime. The impact is amplified by the library's widespread adoption across different platforms and programming languages, making it a potential vector for coordinated denial of service attacks against multiple targets simultaneously.
Mitigation strategies for CVE-2019-20056 primarily focus on updating to patched versions of the stb image loader library. Software vendors and developers should immediately upgrade to versions that address this assertion failure issue, as the maintainers of the library have released fixes to resolve the vulnerability. Organizations should conduct thorough testing of updated libraries to ensure compatibility with existing applications while maintaining security posture. Additionally, implementing input validation measures and sanitization of image files before processing can provide additional defense-in-depth layers. Security monitoring should include detection of unusual application crashes or termination patterns that might indicate exploitation attempts. The vulnerability also highlights the importance of proper error handling and robust input validation in image processing libraries, as outlined in ATT&CK technique T1499.200 which covers the use of application or system binaries for denial of service. Organizations should also consider implementing network segmentation and access controls to limit exposure of applications that process external image data, reducing the attack surface for potential exploitation of this and similar vulnerabilities.