CVE-2025-32024 in imagemeta
Summary
by MITRE • 04/08/2025
bep/imagemeta is a Go library for reading EXIF, IPTC and XMP image meta data from JPEG, TIFF, PNG, and WebP files. The EXIF data format allows for defining excessively large data structures in relatively small payloads. Before v0.10.0, If you didn't trust the input images, this could be abused to construct denial-of-service attacks. v0.10.0 added LimitNumTags (default 5000) and LimitTagSize (default 10000) options.
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Analysis
by VulDB Data Team • 04/08/2025
The vulnerability identified as CVE-2025-32024 affects the bep/imagemeta Go library, which is designed to extract metadata from various image formats including JPEG, TIFF, PNG, and WebP files. This library serves as a critical component in applications that process user-uploaded images or perform automated metadata analysis, making it a potential target for malicious actors seeking to disrupt system operations. The core issue stems from the EXIF data format's inherent capability to define excessively large data structures within relatively small file payloads, a characteristic that becomes exploitable when proper input validation and size limitations are not implemented.
The technical flaw manifests in the library's handling of EXIF metadata structures that can contain oversized data fields, allowing attackers to craft malicious image files with malformed metadata that consumes disproportionate system resources during processing. Without proper safeguards, the library would attempt to parse these oversized structures, leading to memory exhaustion and potential application crashes. This vulnerability specifically impacts versions prior to v0.10.0 where the library lacked built-in protection mechanisms against such malformed data structures. The implementation of LimitNumTags and LimitTagSize parameters in version 0.10.0 addresses this by establishing default thresholds of 5000 tags and 10000 bytes per tag respectively, effectively preventing the processing of excessively large metadata sections.
The operational impact of this vulnerability extends beyond simple denial-of-service conditions, as it can be exploited to consume excessive memory resources, leading to system instability and potential service disruption. Attackers can construct specially crafted image files that trigger resource exhaustion during metadata parsing, making this particularly dangerous in web applications that accept user uploads or in systems processing large volumes of images. The vulnerability aligns with CWE-400, which covers "Uncontrolled Resource Consumption," and represents a classic example of how metadata parsing can become a vector for resource exhaustion attacks. Organizations relying on this library for image processing workflows face significant risk of operational disruption, particularly in environments where system resources are constrained or where multiple concurrent image processing operations occur.
Mitigation strategies should focus on immediate deployment of version 0.10.0 or later, which includes the built-in size limiting parameters that prevent the exploitation of oversized metadata structures. Security teams should also implement additional defensive measures such as setting custom limits for tag counts and sizes based on their specific operational requirements, monitoring for unusual memory consumption patterns during image processing, and implementing proper input validation at the application level. The ATT&CK framework categorizes this type of vulnerability under T1499.004, "Endpoint Denial of Service," as it involves the exploitation of resource consumption vulnerabilities to disrupt service availability. Organizations should also consider implementing sandboxing techniques for image processing and establishing rate limiting for image upload operations to further reduce the attack surface and prevent exploitation attempts.