Skip to content

Latest commit

 

History

History
88 lines (70 loc) · 6.75 KB

attack-tree.md

File metadata and controls

88 lines (70 loc) · 6.75 KB

Attack Tree Analysis for tencent/ncnn

Objective: Compromise application using ncnn by exploiting weaknesses or vulnerabilities within ncnn itself.

Attack Tree Visualization

Compromise Application via ncnn [CRITICAL NODE]
├─── Exploit Model Loading Vulnerabilities [CRITICAL NODE] [HIGH-RISK PATH]
│   └─── Malicious Model Injection [CRITICAL NODE] [HIGH-RISK PATH]
│       └─── Unvalidated Model Source [CRITICAL NODE] [HIGH-RISK PATH]
├─── Exploit Input Data Processing Vulnerabilities in ncnn [CRITICAL NODE] [HIGH-RISK PATH]
│   └─── Adversarial Input Crafting [CRITICAL NODE] [HIGH-RISK PATH]
│       ├─── Model-Specific Adversarial Inputs [HIGH-RISK PATH]
│       └─── Resource Exhaustion via Input Manipulation [HIGH-RISK PATH]
└─── Exploit Dependencies of ncnn [CRITICAL NODE] [HIGH-RISK PATH]
    └─── Vulnerabilities in Third-Party Libraries [CRITICAL NODE] [HIGH-RISK PATH]

Attack Tree Path: Compromise Application via ncnn [CRITICAL NODE]

  • Attack Vector: This is the root goal. Any successful exploitation of ncnn vulnerabilities leading to application compromise falls under this category.
  • Risk: High overall risk as it represents the ultimate objective of the attacker.
  • Mitigation Focus: Implement comprehensive security measures across all identified high-risk paths and critical nodes to prevent achieving this root goal.

Attack Tree Path: Exploit Model Loading Vulnerabilities [CRITICAL NODE] [HIGH-RISK PATH]

  • Attack Vector: Attackers target the model loading process to inject malicious models or manipulate legitimate ones.
  • Risk: High risk due to potential for code execution, data manipulation, or application takeover if a malicious model is loaded and used.
  • Mitigation Focus:

    • Strictly validate model sources.

    • Implement integrity checks for models.

    • Sanitize file paths during model loading.

    • Regularly update ncnn to patch model parsing vulnerabilities.

    • 2.1. Malicious Model Injection [CRITICAL NODE] [HIGH-RISK PATH]:

      • Attack Vector: Injecting a crafted malicious model into the application's model loading process.

      • Risk: Critical risk as malicious models can be designed to exploit ncnn vulnerabilities or contain adversarial logic to compromise the application.

      • Mitigation Focus: Focus on sub-nodes under this path.

      • 2.1.1. Unvalidated Model Source [CRITICAL NODE] [HIGH-RISK PATH]:

        • Attack Vector: Loading models from untrusted sources without proper validation (e.g., user uploads, public internet without integrity checks).
        • Risk: Significant risk, easily exploitable by beginner attackers.
        • Mitigation Focus:
          • Actionable Insight: Implement strict model source validation. Only load models from trusted, controlled sources. Use cryptographic signatures to verify model integrity and authenticity.

Attack Tree Path: Exploit Input Data Processing Vulnerabilities in ncnn [CRITICAL NODE] [HIGH-RISK PATH]

  • Attack Vector: Attackers craft malicious input data to exploit vulnerabilities in ncnn's input processing or the neural network model itself.
  • Risk: Moderate to Critical risk, depending on the specific vulnerability and application context. Can lead to denial of service, incorrect application behavior, or potentially more severe exploits.
  • Mitigation Focus:

    • Input sanitization and validation.

    • Resource limits for ncnn processes.

    • Understanding and mitigating model-specific adversarial input vulnerabilities.

    • Regularly update ncnn to patch input processing vulnerabilities.

    • 3.1. Adversarial Input Crafting [CRITICAL NODE] [HIGH-RISK PATH]:

      • Attack Vector: Crafting specific input data to trigger vulnerabilities or unexpected behavior.

      • Risk: Moderate to Critical risk, depending on the specific sub-attack.

      • Mitigation Focus: Focus on sub-nodes under this path.

      • 3.1.1. Model-Specific Adversarial Inputs [HIGH-RISK PATH]:

        • Attack Vector: Exploiting weaknesses in the specific neural network model used with ncnn by crafting adversarial inputs.
        • Risk: Moderate risk, can lead to incorrect application behavior if the application relies on accurate model outputs.
        • Mitigation Focus:
          • Actionable Insight: Understand the limitations and potential vulnerabilities of the chosen neural network models. Consider using adversarial training techniques to improve model robustness against adversarial inputs. Monitor ncnn's output for anomalies and unexpected results.

      • 3.1.2. Resource Exhaustion via Input Manipulation [HIGH-RISK PATH]:

        • Attack Vector: Sending inputs designed to cause ncnn to consume excessive resources (CPU, memory, GPU), leading to denial of service.
        • Risk: Moderate risk, can disrupt application availability.
        • Mitigation Focus:
          • Actionable Insight: Implement resource limits and monitoring for ncnn processes. Set timeouts for inference operations. Monitor resource usage and detect anomalies that might indicate a resource exhaustion attack.

Attack Tree Path: Exploit Dependencies of ncnn [CRITICAL NODE] [HIGH-RISK PATH]

  • Attack Vector: Exploiting known vulnerabilities in third-party libraries that ncnn depends on.
  • Risk: Critical risk, as vulnerabilities in dependencies can be easily exploited if not patched.
  • Mitigation Focus:

    • Maintain an inventory of ncnn's dependencies.

    • Regularly update dependencies to their latest versions.

    • Use dependency scanning tools to identify vulnerable dependencies.

    • 4.1. Vulnerabilities in Third-Party Libraries [CRITICAL NODE] [HIGH-RISK PATH]:

      • Attack Vector: Exploiting known vulnerabilities in libraries like Vulkan, protobuf, etc., used by ncnn.
      • Risk: Critical risk, easily exploitable if dependencies are outdated.
      • Mitigation Focus:
        • Actionable Insight: Maintain an inventory of ncnn's dependencies. Regularly update dependencies to their latest versions to patch known vulnerabilities. Use dependency scanning tools to identify vulnerable dependencies.