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Attack Tree Analysis for opencv/opencv-python

Objective: To execute arbitrary code on the server or client machine running the opencv-python application, leading to data exfiltration, system compromise, or denial of service.

Attack Tree Visualization

                                 Execute Arbitrary Code [CRITICAL]
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    1.  Vulnerabilities in OpenCV Core (C++)                  2.  Vulnerabilities in Python Bindings
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    ------------------------------                      -------------------------------------------------
    |             |              |                      |                               |

1.1 Buffer 1.2 Integer 1.3 Use-After-Free 2.1 Deserialization 2.3 Python-Specific Overflows Overflows (or similar Vulnerabilities Vulnerabilities memory corruption) (e.g., Pickle) (e.g., input validation) | | | | | 1.1.1 Crafted 1.2.1 Malformed 1.3.1 Triggering 2.1.1 Supplying 2.3.1 Unvalidated Image/Video Image/Video race conditions maliciously crafted image/video paths (e.g., large (e.g., very serialized data (leading to path dimensions, large/small (e.g., via traversal or invalid pixel values) cv2.FileStorage) file inclusion) [HIGH RISK] headers) [HIGH RISK] [CRITICAL] | | | | | [CRITICAL] [CRITICAL] [CRITICAL] [CRITICAL] [CRITICAL] | 2.3.2 Unsanitized numeric input (leading to integer overflows) | [CRITICAL] | 2.3.3 Exploiting vulnerabilities in dependent libraries | [CRITICAL]

Attack Tree Path: 1. Vulnerabilities in OpenCV Core (C++)

  • 1.1 Buffer Overflows

    • 1.1.1 Crafted Image/Video (e.g., large dimensions, invalid headers) [CRITICAL]
      • Description: The attacker crafts a malicious image or video file with manipulated dimensions, pixel data, or file headers. When OpenCV processes this file, it writes data beyond the allocated buffer, overwriting adjacent memory.
      • Exploitation: By carefully controlling the overwritten data, the attacker can redirect program execution to their own malicious code.
      • Example: An image with an extremely large width value could cause a buffer overflow when OpenCV attempts to allocate memory for the image data.

  • 1.2 Integer Overflows

    • 1.2.1 Malformed Image/Video (e.g., very large/small pixel values) [CRITICAL]
      • Description: The attacker provides input with extremely large or small integer values (e.g., for image dimensions or pixel calculations). When these values are used in calculations within OpenCV, they cause an integer overflow.
      • Exploitation: Integer overflows can lead to incorrect memory allocation sizes or out-of-bounds memory access, which can be exploited to achieve code execution.
      • Example: A very large height value, when multiplied by the width and bytes per pixel, could result in an integer overflow, leading to a smaller-than-expected memory allocation and a subsequent buffer overflow.

  • 1.3 Use-After-Free (and similar memory corruption)

    • 1.3.1 Triggering race conditions involving OpenCV objects (e.g., Mat, VideoCapture) [CRITICAL]
      • Description: In a multi-threaded application, the attacker exploits a race condition to access an OpenCV object (like a Mat or VideoCapture) after it has been freed.
      • Exploitation: Accessing freed memory can lead to unpredictable behavior, including crashes or, more seriously, the execution of arbitrary code if the attacker can control the contents of the freed memory.
      • Example: One thread frees a Mat object while another thread is still using it. If the memory is reallocated and filled with attacker-controlled data before the second thread accesses it, the attacker could gain control.

Attack Tree Path: 2. Vulnerabilities in Python Bindings

  • 2.1 Deserialization Vulnerabilities

    • 2.1.1 Supplying maliciously crafted serialized data (e.g., via cv2.FileStorage) [HIGH RISK] [CRITICAL]
      • Description: The attacker provides a maliciously crafted serialized data file (e.g., YAML or XML) to an OpenCV function that deserializes data, such as cv2.FileStorage.
      • Exploitation: The attacker includes a payload in the serialized data that, when deserialized, executes arbitrary code. This is a classic deserialization vulnerability.
      • Example: A YAML file containing a malicious object that, when instantiated during deserialization, executes a system command.

  • 2.3 Python-Specific Vulnerabilities

    • 2.3.1 Unvalidated image/video paths (leading to path traversal or file inclusion) [HIGH RISK] [CRITICAL]

      • Description: The application takes file paths as input from an untrusted source and passes them directly to OpenCV functions (e.g., cv2.imread, cv2.VideoCapture) without proper sanitization.
      • Exploitation: The attacker uses path traversal sequences (e.g., ../) to access files outside the intended directory. This can allow them to read sensitive files or, if the application allows including files, to execute arbitrary code.
      • Example: An attacker provides a path like ../../../../etc/passwd to read the system's password file. Or, they might provide a path to a PHP file they've uploaded, causing the server to execute their code.

    • 2.3.2 Unsanitized numeric input (leading to integer overflows within Python wrapper logic) [CRITICAL]

      • Description: The application takes numeric input from an untrusted source and uses it in calculations within the Python wrapper logic before passing it to OpenCV's core functions. If this input is not properly validated, it can lead to integer overflows.
      • Exploitation: Integer overflows in the Python layer can lead to unexpected behavior, potentially creating vulnerabilities that can be further exploited. This might be used to bypass size checks or to create conditions favorable for other attacks.
      • Example: An attacker provides a very large number as input to a function that calculates the size of a buffer to be allocated. If the calculation overflows, the allocated buffer might be too small, leading to a buffer overflow when data is copied into it.

    • 2.3.3 Exploiting vulnerabilities in dependent libraries (e.g., NumPy vulnerabilities triggered via OpenCV calls) [CRITICAL]

      • Description: opencv-python relies on other libraries, most notably NumPy. Vulnerabilities in these libraries can be triggered through OpenCV calls.
      • Exploitation: The attacker crafts input that, when processed by OpenCV, triggers a vulnerability in a dependent library (e.g., a buffer overflow in NumPy).
      • Example: A specific combination of NumPy array dimensions and data types, when passed to an OpenCV function, might trigger a known vulnerability in NumPy's array handling code.