CVE-2025-53644: OpenCV Buffer Overflow Vulnerability

CVE-2025-53644 Overview

CVE-2025-53644 is an uninitialized memory use vulnerability affecting the OpenCV (Open Source Computer Vision Library) in versions 4.10.0 and 4.11.0. The vulnerability stems from an uninitialized pointer variable on the stack that may lead to arbitrary heap buffer write when processing crafted JPEG images. This flaw exists in the OpenJPEG decoding component integrated within OpenCV.

Critical Impact

Successful exploitation could allow an attacker to achieve arbitrary heap buffer writes through specially crafted JPEG images, potentially leading to code execution or application crashes on systems processing untrusted image data.

Affected Products

• OpenCV version 4.10.0
• OpenCV version 4.11.0
• Applications and systems utilizing vulnerable OpenCV versions for JPEG image processing

Discovery Timeline

• 2025-07-17 - CVE-2025-53644 published to NVD
• 2025-10-17 - Last updated in NVD database

Technical Details for CVE-2025-53644

Vulnerability Analysis

This vulnerability is classified as CWE-457 (Use of Uninitialized Variable). The flaw resides in the OpenJPEG decoding component (3rdparty/openjpeg/openjp2/jp2.c) that OpenCV incorporates for JPEG 2000 image processing. When processing a specially crafted JPEG image, an uninitialized pointer variable on the stack can be dereferenced, leading to arbitrary writes to heap memory.

The vulnerability requires local access and involves high attack complexity. An attacker must craft a malicious JPEG image and induce the target application to process it. If successfully exploited, this vulnerability could result in high impact to confidentiality, integrity, and availability of the affected system.

Root Cause

The root cause lies in improper initialization of a pointer variable within the JP2 decoding function. The code failed to properly check the return status of a decoding operation before accessing the image pointer. When the decoding operation fails, the pointer may remain uninitialized, yet subsequent code paths could still attempt to use it for memory operations. This creates a scenario where an attacker-controlled value could be written to arbitrary heap locations.

Attack Vector

The attack requires local access to the target system, where an attacker must convince a user or automated process to open a maliciously crafted JPEG image using an application built with vulnerable OpenCV versions. Potential attack scenarios include:

1. Delivering malicious images via email attachments to applications with automated image processing
2. Uploading crafted images to web services using OpenCV for image manipulation
3. Targeting computer vision applications that process images from untrusted sources

The security patch addresses the vulnerability by properly checking the return value (ret) before accessing the image pointer:

                               p_image,
                               p_manager);
 
-    if (p_image && *p_image) {
+    if (ret && p_image && *p_image) {
         /* Set Image Color Space */
         if (jp2->enumcs == 16) {
             (*p_image)->color_space = OPJ_CLRSPC_SRGB;

Source: GitHub Commit Changes

Detection Methods for CVE-2025-53644

Indicators of Compromise

• Unexpected application crashes when processing JPEG or JPEG 2000 images
• Memory corruption errors or segmentation faults in OpenCV-dependent applications
• Unusual heap allocation patterns or memory access violations logged during image processing operations

Detection Strategies

• Inventory all applications and dependencies using OpenCV versions 4.10.0 or 4.11.0
• Implement file integrity monitoring for OpenCV library files to detect tampering
• Deploy application-level monitoring to detect abnormal behavior during image processing operations
• Use static analysis tools to identify usage of vulnerable OpenCV versions in codebases

Monitoring Recommendations

• Enable crash dump collection for applications using OpenCV to capture exploitation attempts
• Monitor system logs for recurring memory-related errors during image processing workflows
• Implement runtime application self-protection (RASP) to detect and block memory corruption attacks
• Set up alerting for unexpected process terminations in computer vision pipelines

How to Mitigate CVE-2025-53644

Immediate Actions Required

• Upgrade OpenCV to version 4.12.0 or later which contains the security fix
• Audit all applications and systems to identify vulnerable OpenCV installations
• Implement input validation to restrict image processing to trusted sources where possible
• Consider sandboxing applications that process untrusted image files

Patch Information

OpenCV has released version 4.12.0 which includes the security fix for this vulnerability. The patch ensures proper validation of the decoding return status before accessing the image pointer, preventing the use of uninitialized memory. The fix can be found in commit a39db41390de546d18962ee1278bd6dbb715f466.

For detailed information, refer to:

• OpenCV Release 4.12.0
• GitHub Security Advisory GHSL-2025-057
• GitHub Issue Discussion

Workarounds

• Restrict image processing to trusted sources and validate image files before processing
• Implement application sandboxing to limit the impact of potential exploitation
• Use memory-safe compilation flags and runtime protections (ASLR, stack canaries) to increase exploitation difficulty
• Consider using alternative image processing libraries for handling untrusted JPEG content until patching is complete

# Upgrade OpenCV to patched version
pip install opencv-python==4.12.0

# Verify installed version
python -c "import cv2; print(cv2.__version__)"