Skip to content

Latest commit

 

History

History
87 lines (64 loc) · 194 KB

sec-design-deep-analysis.md

File metadata and controls

87 lines (64 loc) · 194 KB

Deep Security Analysis of ncnn

1. Objective, Scope, and Methodology

Objective: To conduct a thorough security analysis of the ncnn framework, focusing on its key components, identifying potential vulnerabilities, and providing actionable mitigation strategies. The analysis will consider the framework's design, build process, deployment options, and intended use cases, with a particular emphasis on the security implications for mobile and embedded applications. We aim to identify weaknesses that could lead to model compromise, data leakage, denial of service, or code execution vulnerabilities.

Scope:

  • Core ncnn framework: This includes the model loading and parsing logic (Model Manager), the inference engine, and the computation backends (CPU, GPU, DSP).
  • API: The public interface exposed to developers.
  • Build process: The CI/CD pipeline, including static analysis and fuzzing.
  • Deployment: Focus on dynamic linking, as it's the chosen deployment method.
  • Supported data formats: The formats used for models and input data.
  • Dependencies: The (minimal) external dependencies of ncnn.

Methodology:

  1. Architecture and Data Flow Analysis: Based on the provided C4 diagrams and documentation, we will infer the architecture, components, and data flow within ncnn. This includes understanding how models are loaded, parsed, and executed, and how data flows between the application, ncnn, and the hardware.
  2. Threat Modeling: We will identify potential threats based on the architecture, data flow, and business risks. We will consider threats related to model integrity, data confidentiality, availability, and potential for code execution. We will use STRIDE (Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, Elevation of Privilege) as a guiding framework.
  3. Vulnerability Analysis: We will analyze each component for potential vulnerabilities, considering common C/C++ vulnerabilities (buffer overflows, integer overflows, format string bugs, etc.) and vulnerabilities specific to neural network frameworks.
  4. Mitigation Strategy Recommendation: For each identified vulnerability, we will propose specific and actionable mitigation strategies tailored to ncnn.
  5. Security Control Review: We will assess the effectiveness of existing security controls and recommend improvements or additions.

2. Security Implications of Key Components

Here's a breakdown of the security implications of each key component, based on the provided design review:

  • ncnn API:

    • Threats: Malicious input data (crafted tensors or model files) could exploit vulnerabilities in the API, leading to denial of service, code execution, or information disclosure. Insufficient input validation could allow attackers to bypass security checks.
    • Vulnerabilities: Buffer overflows, integer overflows, format string vulnerabilities, injection vulnerabilities (if the API allows passing strings that are later used in system calls), and denial-of-service vulnerabilities due to resource exhaustion.
    • Mitigation: Rigorous input validation (size, type, range checks) for all API parameters. Use of safe string handling functions. Resource limits (e.g., maximum model size, maximum memory allocation). Fuzz testing the API extensively. Employing Address Space Layout Randomization (ASLR) and Data Execution Prevention (DEP)/NX bit, which are OS-level protections, but ncnn should be compiled with support for them.

  • Model Manager:

    • Threats: Loading a maliciously crafted model file could lead to code execution or denial of service. Tampering with a legitimate model file could lead to incorrect inference results or other undesirable behavior.
    • Vulnerabilities: Vulnerabilities in the model parsing logic (e.g., buffer overflows, integer overflows, type confusion) could be exploited by a malicious model file. Lack of integrity checks could allow attackers to modify model files without detection.
    • Mitigation: Implement robust model parsing logic with extensive validation of all model parameters and structure. Use a memory-safe language or memory-safe libraries for parsing, if possible. Implement cryptographic checksums (e.g., SHA-256) or digital signatures to verify model integrity. Reject models that fail integrity checks. Fuzz test the model loading process with various corrupted and malformed model files. Consider using a formal model specification to define valid model structures and use this specification to generate parsing code.

  • Inference Engine:

    • Threats: Exploiting vulnerabilities in the inference engine could lead to code execution, denial of service, or potentially information disclosure (e.g., through side-channel attacks).
    • Vulnerabilities: Arithmetic errors (integer overflows/underflows, floating-point exceptions) during computation. Memory corruption vulnerabilities (buffer overflows, use-after-free) in the core computation logic. Side-channel vulnerabilities (timing attacks, power analysis attacks) that could leak information about the model or input data.
    • Mitigation: Thorough input validation before performing computations. Use of safe arithmetic operations (e.g., checked arithmetic libraries). Rigorous memory management practices. Fuzz testing the inference engine with a wide range of inputs and model parameters. Consider using constant-time algorithms to mitigate timing attacks, although this may impact performance. Evaluate the potential for power analysis attacks and implement countermeasures if necessary (this is highly dependent on the target hardware and threat model).

  • Computations (CPU, GPU, DSP):

    • Threats: Vulnerabilities in the backend implementations could be exploited to compromise the system. This is particularly relevant for GPU and DSP backends, which may involve complex drivers and libraries.
    • Vulnerabilities: Vulnerabilities in the underlying libraries (e.g., BLAS, cuDNN) used for computation. Driver vulnerabilities. Memory corruption vulnerabilities in the backend-specific code.
    • Mitigation: Keep all backend libraries up to date with the latest security patches. Use memory-safe wrappers around library calls. Perform thorough testing of the backend implementations. If custom kernels are used (e.g., for specific hardware), ensure they are thoroughly reviewed and tested for security vulnerabilities. Consider sandboxing the execution of GPU/DSP code, if possible.

  • Neural Network Model (Data):

    • Threats: Model poisoning (modifying the model to produce incorrect results for specific inputs). Model extraction (stealing the model from the device).
    • Vulnerabilities: Lack of integrity checks allows model tampering. Lack of encryption allows model extraction.
    • Mitigation: Implement model integrity checks (checksums, digital signatures). Consider using model encryption (this is the responsibility of the application using ncnn, but ncnn should be compatible with encrypted models). Explore the use of Trusted Execution Environments (TEEs) or secure enclaves to protect the model.

3. Inferred Architecture, Components, and Data Flow (Detailed)

Based on the codebase structure and documentation, we can infer the following detailed architecture and data flow:

  1. Application Interaction: The application uses the ncnn API (primarily ncnn::Net) to interact with the framework. Key API functions include:

    • load_param and load_model: Load the model architecture and weights.
    • create_extractor: Creates an Extractor object for performing inference.
    • input: Sets the input tensor data.
    • extract: Performs inference and retrieves the output tensor.

  2. Model Loading (Model Manager):

    • ncnn supports two primary model formats: a .param file (text-based, describing the network architecture) and a .bin file (binary, containing the model weights).
    • The load_param function parses the .param file, creating in-memory representations of the layers and their connections. This involves parsing strings and converting them to numerical values.
    • The load_model function reads the .bin file and loads the weights into the corresponding layers. This involves reading binary data and performing type conversions.

  3. Inference Execution (Inference Engine):

    • The Extractor object manages the inference process.
    • The input function copies the input data into ncnn's internal data structures (likely ncnn::Mat).
    • The extract function iterates through the layers of the network, performing the forward pass.
    • For each layer, the appropriate computation function is called (e.g., convolution, pooling, etc.).
    • These computation functions may be implemented using optimized CPU code (e.g., using SIMD instructions), or they may delegate to backend libraries (e.g., Vulkan for GPU).

  4. Data Flow:

    • Input data flows from the application to the Extractor via the input function.
    • The data is then processed sequentially through the layers of the network.
    • Intermediate results are stored in ncnn::Mat objects.
    • The final output is retrieved from the Extractor via the extract function and passed back to the application.

  5. Memory Management: ncnn uses its own memory allocator (ncnn::Allocator) to manage memory for tensors and other internal data structures. This is crucial for performance and portability.

4. Specific Security Considerations and Mitigation Strategies for ncnn

Based on the above analysis, here are specific security considerations and mitigation strategies:

| Threat | Vulnerability | Mitigation Strategy