CVE-2025-43964 Overview
CVE-2025-43964 is an input validation vulnerability in LibRaw, a widely-used library for reading RAW files from digital photo cameras. The vulnerability exists in the phase_one_correct function within decoders/load_mfbacks.cpp, where tag 0x412 processing fails to enforce minimum values for the w0 and w1 parameters. This improper input validation (CWE-1284) can allow attackers to trigger unexpected behavior when processing maliciously crafted RAW image files.
Critical Impact
This vulnerability can be exploited remotely via maliciously crafted image files, potentially leading to memory corruption or denial of service in applications that process untrusted RAW image files using vulnerable versions of LibRaw.
Affected Products
- LibRaw versions prior to 0.21.4
- Applications and image processing tools built with vulnerable LibRaw versions
- Debian and derivative distributions with unpatched LibRaw packages
Discovery Timeline
- 2025-04-21 - CVE-2025-43964 published to NVD
- 2025-11-03 - Last updated in NVD database
Technical Details for CVE-2025-43964
Vulnerability Analysis
The vulnerability resides in the Phase One correction tag processing logic within LibRaw's load_mfbacks.cpp decoder. When processing tag 0x412, the code calculates w0 and w1 values from header data (head[1] * head[3] and head[2] * head[4] respectively). While the original code included upper-bound validation to prevent excessive memory allocation, it lacked minimum value enforcement, creating a potential for exploitation through zero or negative-equivalent values.
The vulnerability affects the memory allocation and subsequent operations that depend on these calculated width values. Without proper validation, a crafted RAW file could bypass existing security checks and trigger undefined behavior in downstream processing.
Root Cause
The root cause is improper input validation (CWE-1284) in the phase_one_correct function. The code enforced maximum bounds for w0 and w1 (checking against 10240000) but failed to verify that these values meet a minimum threshold. This oversight allows specially crafted image metadata to pass validation while containing invalid dimensional parameters that could lead to memory safety issues during subsequent operations.
Attack Vector
The attack vector is network-based, requiring an attacker to deliver a malicious RAW image file to a victim. This could occur through:
- Uploading malicious images to web applications that process RAW files
- Email attachments containing crafted image files
- Malicious content on websites that trigger automatic image processing
- File sharing platforms where users download and process untrusted images
The following patch shows the security fix applied by LibRaw:
unsigned w0 = head[1] * head[3], w1 = head[2] * head[4];
if (w0 > 10240000 || w1 > 10240000)
throw LIBRAW_EXCEPTION_ALLOC;
+ if (w0 < 1 || w1 < 1)
+ throw LIBRAW_EXCEPTION_IO_CORRUPT;
yval[0] = (float *)calloc(head[1] * head[3] + head[2] * head[4], 6);
yval[1] = (float *)(yval[0] + head[1] * head[3]);
xval[0] = (ushort *)(yval[1] + head[2] * head[4]);
Source: GitHub Commit Update
Detection Methods for CVE-2025-43964
Indicators of Compromise
- Unexpected application crashes when processing RAW image files, particularly Phase One camera formats
- Error logs showing LIBRAW_EXCEPTION_ALLOC or memory-related exceptions in LibRaw components
- Anomalous RAW files with malformed tag 0x412 metadata in Phase One correction data
- Increased resource consumption or unusual memory patterns during image processing operations
Detection Strategies
- Implement file integrity monitoring for RAW image processing pipelines to detect malformed inputs
- Deploy application-level logging to capture LibRaw exception handling events and correlate with file sources
- Use static analysis tools to identify applications linked against vulnerable LibRaw versions (prior to 0.21.4)
- Monitor for unusual patterns in image upload functionality that may indicate exploitation attempts
Monitoring Recommendations
- Enable verbose logging in applications utilizing LibRaw to capture processing errors and exceptions
- Configure intrusion detection systems to inspect file uploads for anomalous RAW file structures
- Implement version tracking for LibRaw dependencies across your software inventory
- Set up alerts for repeated image processing failures that may indicate targeted exploitation
How to Mitigate CVE-2025-43964
Immediate Actions Required
- Upgrade LibRaw to version 0.21.4 or later immediately across all affected systems
- Identify and inventory all applications and services that depend on LibRaw for image processing
- Restrict processing of untrusted RAW image files until patching is complete
- Review system and application logs for signs of exploitation attempts against image processing functionality
Patch Information
LibRaw has released version 0.21.4 which addresses this vulnerability by adding minimum value validation for w0 and w1 parameters in the Phase One correction tag processing. The fix ensures that values less than 1 trigger a LIBRAW_EXCEPTION_IO_CORRUPT exception, preventing further processing of malformed data.
Patch details and version comparison are available at:
For Debian-based systems, refer to the Debian LTS Announcement for package update information.
Workarounds
- Implement input validation at the application layer to reject suspicious RAW files before LibRaw processing
- Sandbox image processing operations using containerization or restricted user privileges to limit potential impact
- Disable processing of Phase One RAW formats if not required for business operations until patching is complete
- Deploy web application firewalls configured to inspect and validate uploaded image file metadata
# Configuration example
# Update LibRaw on Debian/Ubuntu systems
sudo apt-get update
sudo apt-get install --only-upgrade libraw23
# Verify installed version
dpkg -l | grep libraw
# For source installations, upgrade to 0.21.4
wget https://www.libraw.org/data/LibRaw-0.21.4.tar.gz
tar -xzf LibRaw-0.21.4.tar.gz
cd LibRaw-0.21.4
./configure && make && sudo make install
Disclaimer: This content was generated using AI. While we strive for accuracy, please verify critical information with official sources.


