Skip to content

Latest commit

 

History

History
57 lines (46 loc) · 5.48 KB

attack-tree.md

File metadata and controls

57 lines (46 loc) · 5.48 KB

Attack Tree Analysis for tencent/ncnn

Objective: Achieve Arbitrary Code Execution (ACE) or other significant impact (DoS, Data Exfiltration) via ncnn

Attack Tree Visualization

Goal: Achieve Arbitrary Code Execution (ACE) or other significant impact (DoS, Data Exfiltration) via ncnn
├── 1. Exploit Vulnerabilities in ncnn Core [HIGH RISK]
│   ├── 1.1 Buffer Overflows/Underflows [HIGH RISK]
│   │   ├── 1.1.1  Craft Malicious Model [CRITICAL]
│   │   └── 1.1.2  Craft Malicious Input Data [CRITICAL]
│   ├── 1.3  Type Confusion
│   │   └── 1.3.2  Exploit Weak Type Handling in Custom Layers [HIGH RISK]
│   ├── 1.4  Use-After-Free [HIGH RISK]
│   │   ├── 1.4.1  Craft Malicious Model or Input [CRITICAL]
├── 2.  Model Poisoning / Data Poisoning
│   ├── 2.1  Supply Malicious Model [HIGH RISK]
│   │   ├── 2.1.1  Social Engineering [HIGH RISK]

Attack Tree Path: 1. Exploit Vulnerabilities in ncnn Core [HIGH RISK]

  • 1.1 Buffer Overflows/Underflows [HIGH RISK]

    • Description: These vulnerabilities occur when data is written outside the allocated memory buffer, potentially overwriting adjacent memory regions. This can lead to arbitrary code execution.
    • 1.1.1 Craft Malicious Model [CRITICAL]
      • Description: The attacker creates a specially crafted ncnn model file (.param and .bin) with manipulated layer parameters (e.g., excessively large dimensions, weights, or biases). When ncnn loads or processes this model, the manipulated parameters trigger a buffer overflow or underflow.
      • Example: A convolution layer with an extremely large kernel size that exceeds the allocated buffer for kernel weights.
      • Mitigation: Strict input validation and size checks during model loading (Net::load_param, Net::load_model). Comprehensive fuzz testing of all layers, especially custom and platform-specific optimized ones.
    • 1.1.2 Craft Malicious Input Data [CRITICAL]
      • Description: The attacker provides specially crafted input data (e.g., an image with unusual dimensions or pixel values) to a loaded ncnn model. This input, combined with the model's parameters, triggers a buffer overflow/underflow during inference.
      • Example: An image with dimensions that, when processed by a specific layer, cause an out-of-bounds write to an internal buffer.
      • Mitigation: Input validation and size limits for all input data. Fuzz testing with a wide range of input data, focusing on layers with dynamic memory allocation. Secure use of external pre-processing libraries.

  • 1.3 Type Confusion

    • 1.3.2 Exploit Weak Type Handling in Custom Layers [HIGH RISK]
      • Description: If the application uses custom layers (layers not part of the standard ncnn library), vulnerabilities in the custom layer's type handling can be exploited. This might involve passing data of an unexpected type to the layer, leading to incorrect memory access or calculations.
      • Example: A custom layer that expects a float input but receives an integer, leading to a misinterpretation of the data and a potential buffer overflow.
      • Mitigation: Thorough code review and testing of custom layer implementations, with a strong focus on type safety. Enforce strict type checking and validation within the custom layer code.

  • 1.4 Use-After-Free [HIGH RISK]

    • Description: This vulnerability occurs when a program attempts to use memory that has already been freed. This can lead to unpredictable behavior, including crashes and arbitrary code execution.
    • 1.4.1 Craft Malicious Model or Input [CRITICAL]
      • Description: The attacker crafts a malicious model or input data that triggers a sequence of memory allocations and deallocations, leading to a situation where ncnn attempts to access memory that has already been freed.
      • Example: A model with a specific layer configuration that causes a memory block to be freed prematurely, followed by another layer attempting to access that same memory block.
      • Mitigation: Use memory safety tools (AddressSanitizer, Valgrind) during development and testing. Careful code review of memory management logic. Ensure thread safety in multi-threaded environments.

Attack Tree Path: 2. Model Poisoning / Data Poisoning

  • 2.1 Supply Malicious Model [HIGH RISK]
    • Description: The attacker tricks the application into loading a pre-trained model that has been intentionally modified to produce incorrect results or exhibit malicious behavior (without necessarily causing a crash or immediate ACE).
    • 2.1.1 Social Engineering [HIGH RISK]
      • Description: The attacker uses social engineering techniques (e.g., phishing, impersonation) to convince the application developers or users to download and use a poisoned model from an untrusted source.
      • Example: The attacker sends an email pretending to be a reputable researcher, offering a "highly optimized" ncnn model that is actually poisoned.
      • Mitigation: Educate developers and users about the risks of using untrusted models. Implement model signature verification. Use trusted model repositories with strong access controls. Use HTTPS with certificate pinning for model downloads.