CVE-2018-20652 in tinyexr
Summary
by MITRE
An attempted excessive memory allocation was discovered in the function tinyexr::AllocateImage in tinyexr.h in tinyexr v0.9.5. Remote attackers could leverage this vulnerability to cause a denial-of-service via crafted input, which leads to an out-of-memory exception.
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Analysis
by VulDB Data Team • 06/22/2023
The vulnerability identified as CVE-2018-20652 resides within the tinyexr library version 0.9.5, specifically within the tinyexr::AllocateImage function located in tinyexr.h. This issue represents a critical memory management flaw that can be exploited by remote attackers to execute denial-of-service attacks. The vulnerability manifests when the library processes crafted input files that contain malformed dimensions or metadata, leading to attempts at allocating excessive memory resources that exceed system capabilities. The tinyexr library is commonly used for reading and writing OpenEXR image files, which are widely employed in professional graphics and visual effects applications where memory efficiency and robustness are paramount.
The technical flaw stems from inadequate input validation and memory allocation bounds checking within the AllocateImage function. When processing malformed EXR files, the function fails to properly validate the dimensions specified in the file headers, allowing attackers to specify arbitrarily large image dimensions that result in massive memory allocation requests. This vulnerability directly maps to CWE-770, which addresses allocation of resources without proper size limits, and represents a classic example of unchecked memory allocation that can lead to system instability. The function does not implement reasonable upper bounds for memory allocation requests, making it susceptible to exploitation through carefully crafted input that triggers excessive memory consumption.
The operational impact of this vulnerability extends beyond simple service disruption, as it can potentially crash applications that rely on tinyexr for image processing or cause systems to become unresponsive due to memory exhaustion. In environments where applications process untrusted image files from external sources, such as web applications or content management systems, this vulnerability presents a significant risk of system compromise. Attackers can exploit this weakness by uploading or providing malicious EXR files that, when processed by vulnerable applications, trigger the excessive memory allocation behavior. The out-of-memory exception that results from this exploitation can cause applications to terminate abruptly, leading to complete service unavailability and potential data loss. This vulnerability affects any system using tinyexr v0.9.5 or earlier versions, particularly those in media processing pipelines, digital asset management systems, and graphics rendering environments.
Mitigation strategies for CVE-2018-20652 should prioritize immediate patching of the tinyexr library to version 1.0.0 or later, which contains fixed memory allocation routines with proper bounds checking. Organizations should implement input validation measures that enforce reasonable limits on image dimensions and file sizes before processing any EXR files. The ATT&CK framework categorizes this vulnerability under T1499.004, which covers Network Denial of Service, and T1059.001, which involves command and scripting interpreter. Security teams should deploy monitoring solutions to detect unusual memory allocation patterns and implement sandboxing techniques for processing untrusted image files. Additionally, network segmentation and access controls should be enforced to limit exposure of systems that process EXR files, while regular security audits should verify that all applications using tinyexr have been updated to secure versions. The vulnerability also highlights the importance of implementing proper error handling and resource management practices in third-party libraries, as demonstrated by the ATT&CK technique T1595.001 for reconnaissance through network scanning and T1046 for network service scanning that could be used to identify vulnerable systems.