CVE-2025-43966 Overview
CVE-2025-43966 is a NULL pointer dereference vulnerability discovered in libheif, an open-source library for reading and writing HEIF and AVIF image formats. The vulnerability exists in the ImageItem_iden function within the image-items/iden.cc source file. When processing malformed or specially crafted image files, the library fails to properly validate that an image object exists before dereferencing it, leading to application crashes and denial of service conditions.
Critical Impact
Applications utilizing libheif versions prior to 1.19.6 are susceptible to denial of service attacks through remotely delivered malicious image files. Attackers can exploit this vulnerability via network-accessible services that process HEIF/AVIF images.
Affected Products
- struktur libheif versions prior to 1.19.6
Discovery Timeline
- 2025-04-21 - CVE-2025-43966 published to NVD
- 2025-05-08 - Last updated in NVD database
Technical Details for CVE-2025-43966
Vulnerability Analysis
This vulnerability is classified as CWE-476 (NULL Pointer Dereference). The flaw occurs within the ImageItem_iden class when attempting to retrieve a coded image colorspace. The vulnerable code path calls get_context()->get_image() to obtain an image object, but proceeds to invoke methods on this object without first verifying that the returned pointer is valid.
When processing a malformed HEIF/AVIF file that references a non-existing image item, the get_image() function returns a NULL pointer. The subsequent call to image->get_coded_image_colorspace() then dereferences this NULL pointer, causing an immediate crash of the application.
This vulnerability can be exploited remotely without authentication by delivering a malicious image file to any application or service that uses libheif for image processing. The attack requires no user privileges and no interaction beyond the target system processing the crafted file.
Root Cause
The root cause is missing NULL pointer validation after retrieving an image object from the context. The code assumed that get_image() would always return a valid pointer when processing image items, without accounting for malformed input files that could reference non-existent items.
Attack Vector
An attacker can exploit this vulnerability by crafting a malicious HEIF or AVIF image file with invalid internal references to non-existent image items. When a vulnerable application attempts to process this file, the NULL pointer dereference occurs, causing the application to crash. This attack vector is network-accessible, meaning attackers can deliver malicious images via web uploads, email attachments, or any other mechanism that results in image processing.
}
auto image = get_context()->get_image(child, true);
+ if (!image) {
+ return Error{heif_error_Invalid_input,
+ heif_suberror_Nonexisting_item_referenced};
+ }
+
return image->get_coded_image_colorspace(out_colorspace, out_chroma);
}
Source: GitHub Commit Changes
The security patch adds proper NULL pointer validation before dereferencing the image object, returning an appropriate error when an invalid item reference is encountered.
Detection Methods for CVE-2025-43966
Indicators of Compromise
- Application crashes or unexpected terminations when processing HEIF/AVIF image files
- Segmentation fault logs in system error logs associated with libheif-dependent processes
- Repeated crash patterns in image processing services with stack traces pointing to ImageItem_iden or iden.cc
- Unusual volumes of malformed image file uploads targeting image processing endpoints
Detection Strategies
- Monitor application crash logs for segmentation faults in processes that utilize libheif for image processing
- Implement file integrity monitoring to detect malformed HEIF/AVIF files with invalid internal references
- Deploy runtime application self-protection (RASP) to detect NULL pointer dereference attempts in real-time
- Use static analysis tools to identify libheif versions in deployed applications and flag versions prior to 1.19.6
Monitoring Recommendations
- Enable detailed crash reporting for applications processing image files to capture stack traces
- Configure alerting for repeated application restarts or crashes in image processing pipelines
- Monitor network traffic for unusual patterns of HEIF/AVIF file uploads from suspicious sources
- Implement logging at the application level to track image processing failures and error codes
How to Mitigate CVE-2025-43966
Immediate Actions Required
- Upgrade libheif to version 1.19.6 or later immediately to address this vulnerability
- Audit all systems and applications to identify deployments using vulnerable libheif versions
- Implement input validation to reject potentially malformed image files before processing
- Consider temporarily disabling HEIF/AVIF processing capabilities if immediate patching is not feasible
Patch Information
The vulnerability has been addressed in libheif version 1.19.6. The fix adds proper NULL pointer validation in the ImageItem_iden class before attempting to retrieve the coded image colorspace. Organizations should update to this version or later to remediate the vulnerability. The patch can be reviewed in the GitHub Commit Changes, and the complete version differences are available in the GitHub Version Comparison.
Workarounds
- Implement strict input validation to filter potentially malicious image files before they reach libheif processing
- Deploy sandboxing or containerization for image processing services to limit crash impact
- Use process isolation to prevent denial of service from affecting other system components
- Consider implementing request rate limiting for image upload endpoints to reduce attack surface
# Check installed libheif version
pkg-config --modversion libheif
# Update libheif on Debian/Ubuntu systems
sudo apt update && sudo apt install libheif1
# For systems building from source, update to patched version
git clone https://github.com/strukturag/libheif.git
cd libheif
git checkout v1.19.6
mkdir build && cd build
cmake ..
make && sudo make install
Disclaimer: This content was generated using AI. While we strive for accuracy, please verify critical information with official sources.

