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Attack Tree Analysis for dmlc/gluon-cv

Objective: To cause a denial-of-service (DoS) or execute arbitrary code on the application server by exploiting vulnerabilities in the Gluon-CV library or its dependencies.

Attack Tree Visualization

Compromise Application using Gluon-CV |

| |

  1. Denial of Service (DoS) 3. Arbitrary Code Execution (ACE) [CRITICAL] | |

| | 1.2 Input 3.1 Model Data Loading DoS ACE | | 1.2.1 [HIGH RISK] 3.1.1 [HIGH RISK][CRITICAL] Maliciously Pickle Crafted Deserial. Input Vuln. (if used)

Attack Tree Path: 1. Denial of Service (DoS) via Input Data

  • 1.2.1 Maliciously Crafted Input [HIGH RISK]

    • Description: The attacker provides input data specifically designed to consume excessive resources, leading to a denial-of-service condition. This could involve:

      • Extremely large images (in terms of dimensions or file size).
      • Images with an unusually high number of objects to be detected or classified.
      • Images with crafted pixel values that trigger worst-case performance in the processing pipeline (though this is more difficult to achieve).
      • Other forms of input data (if the application uses Gluon-CV for tasks beyond image processing) that are designed to be computationally expensive.

    • Likelihood: High. Creating large images or inputs with many objects is trivial.

    • Impact: High. The application becomes unresponsive or crashes, preventing legitimate users from accessing it.

    • Effort: Low. Minimal resources are required to generate malicious input.

    • Skill Level: Novice. No specialized knowledge of computer vision or security is needed.

    • Detection Difficulty: Medium. Requires monitoring input sizes, resource usage (CPU, memory), and application response times. An unusually large input or a sudden spike in resource consumption could indicate an attack.

Attack Tree Path: 2. Arbitrary Code Execution (ACE) via Model Loading

  • 3.1.1 Pickle Deserialization Vulnerability [HIGH RISK][CRITICAL]

    • Description: If Gluon-CV uses the pickle module (or a similar vulnerable serialization library) to load models, and if it loads models from untrusted sources (e.g., user uploads, external URLs), an attacker can craft a malicious model file. When this file is deserialized, it executes arbitrary code on the server. This is a classic and extremely dangerous vulnerability.

    • Likelihood: Very High if Pickle is used with untrusted input. The vulnerability is inherent to the insecure deserialization process.

    • Impact: Very High. Complete system compromise. The attacker gains full control over the application and potentially the underlying operating system. They can steal data, install malware, or use the compromised system for other malicious purposes.

    • Effort: Low. Publicly available exploits and tools can generate malicious Pickle payloads.

    • Skill Level: Intermediate. While generating the payload is easy, understanding the underlying vulnerability and how to deploy the attack effectively requires some knowledge of Python and security concepts.

    • Detection Difficulty: Medium. Requires monitoring file access, process execution, and network activity. Intrusion detection systems (IDS) and security information and event management (SIEM) systems can help detect suspicious behavior. However, a skilled attacker might be able to evade detection. File integrity monitoring can also help detect unauthorized changes to model files.