threat-modeling.md

Threat Model Analysis for opencv/opencv-python

Threat: Native Code Buffer Overflow in Image Decoding

• Description: An attacker crafts a malicious image file (e.g., JPEG, PNG) that, when processed by OpenCV's image decoding functions, causes a buffer overflow in the underlying native C/C++ code. The attacker might exploit this to overwrite memory, potentially leading to arbitrary code execution.
• Impact: Remote Code Execution (RCE), Denial of Service (DoS), Data Corruption. Successful RCE allows full control of the server/application. DoS can occur due to crashes or resource exhaustion.
• Affected OpenCV-Python Component: cv2.imread, cv2.imdecode, and image loading functions using native decoders (JPEG, PNG, TIFF modules).
• Risk Severity: Critical
• Mitigation Strategies:
  • Regularly update opencv-python to the latest stable version for security patches.
  • Validate image file types and sizes before processing. Consider sanitizing images with external libraries before OpenCV.
  • Isolate image processing in sandboxed environments or containers.
  • Use memory safety tools (ASan, MSan) during development to detect memory errors.

Threat: Integer Overflow in Image Processing Operations

• Description: An attacker provides input images or parameters that cause integer overflows in OpenCV's image processing algorithms (resizing, filtering, arithmetic). This can lead to memory corruption or denial of service, potentially exploitable. For example, large image dimensions could cause overflows during internal calculations.
• Impact: Denial of Service (DoS), Memory Corruption, Potential for Exploitation. DoS from crashes or incorrect memory allocation. Memory corruption can lead to unpredictable application behavior.
• Affected OpenCV-Python Component: cv2.resize, cv2.filter2D, cv2.add, cv2.subtract, cv2.multiply, and other image processing functions, especially with large images or extreme parameters.
• Risk Severity: High
• Mitigation Strategies:
  • Validate input image dimensions and parameters to ensure they are within reasonable ranges.
  • Implement robust error handling for OpenCV operations to catch exceptions from overflows.
  • Limit the maximum size and dimensions of processed images.
  • Review code using OpenCV-Python for potential integer overflow vulnerabilities.

Threat: Supply Chain Compromise of opencv-python Package

• Description: An attacker compromises the opencv-python package on package repositories (e.g., PyPI) or its dependencies by injecting malicious code. Upon installation or update, this code executes, potentially granting attacker access to developer machines or deployed environments.
• Impact: Remote Code Execution (RCE), Data Breach, Supply Chain Disruption. Attackers can gain control, steal data, or disrupt development/deployment.
• Affected OpenCV-Python Component: The opencv-python package and its installation process.
• Risk Severity: High
• Mitigation Strategies:
  • Download opencv-python from official, trusted repositories like PyPI.
  • Use dependency locking (e.g., requirements.txt, poetry.lock) for consistent builds.
  • Verify package integrity using package manager features (e.g., pip --verify-hashes).
  • Regularly audit dependencies, including opencv-python, for vulnerabilities.
  • Employ secure development environments and practices to minimize supply chain risks.