attack-surface.md

Attack Surface Analysis for tencent/ncnn

Attack Surface: 1. Malicious Model Loading

• Description: Attackers provide crafted .param and .bin files designed to exploit vulnerabilities in ncnn's model loading or parsing process, leading to code execution or denial of service. This is the primary attack vector.
  • How ncnn Contributes: ncnn's core functionality is loading and executing these model files. The .param and .bin file formats and the parsing logic are specific to ncnn. Vulnerabilities in this process are directly attributable to ncnn.
  • Example: An attacker crafts a .param file with an invalid layer configuration that triggers a buffer overflow or integer overflow during parsing within ncnn, leading to arbitrary code execution. Alternatively, a .param file with extremely large layer sizes could cause a denial-of-service due to memory exhaustion when ncnn attempts to allocate memory.
  • Impact: Arbitrary Code Execution (ACE), Denial of Service (DoS).
  • Risk Severity: Critical
  • Mitigation Strategies:
    • Strict Model Source Control: Only load models from trusted, verified sources. Implement strong integrity checks (cryptographic signatures, checksums, and a chain of trust) for model files. Never load models directly from untrusted input.
    • Robust Model Validation: Implement a pre-loading validator that parses the .param file before passing it to ncnn. This validator must:
      • Enforce strict, hard-coded limits on layer sizes, the number of layers, supported operation types, and other model parameters. Reject any model exceeding these limits. This is the most important mitigation.
      • Use a whitelist of allowed layer types and configurations, rejecting anything not on the whitelist.
      • Consider static analysis techniques to identify potentially dangerous patterns in the model structure, if feasible.
    • Sandboxing: Execute ncnn's model loading and inference within a sandboxed environment (e.g., a container) with restricted privileges and resource quotas (CPU, memory). This limits the impact of a successful exploit.

Attack Surface: 2. Inference Engine Exploitation

• Description: Attackers exploit vulnerabilities within ncnn's inference engine (the code that executes the loaded model) using carefully crafted input data, potentially in combination with a specifically designed (but not necessarily overtly malicious) model. This exploits bugs in ncnn's runtime.
  • How ncnn Contributes: The inference engine is entirely ncnn code. Any vulnerabilities in this code are directly attributable to ncnn's implementation.
  • Example: An attacker provides input data that, when processed by a specific layer type within a seemingly legitimate model, triggers a use-after-free error or a buffer overflow in ncnn's memory management during inference, leading to a crash or potentially arbitrary code execution.
  • Impact: Arbitrary Code Execution (ACE), Denial of Service (DoS).
  • Risk Severity: High
  • Mitigation Strategies:
    • Fuzz Testing: Conduct extensive fuzz testing of the ncnn inference engine. Use a wide variety of models (including those with unusual or edge-case configurations) and a diverse range of input data. This is crucial for finding subtle bugs.
    • Code Auditing: Regularly audit the ncnn codebase, with a particular focus on the inference engine, memory management routines, and any code that handles input data or interacts with model parameters.
    • Sandboxing: As with model loading, sandboxing the inference process is essential to contain the impact of any successful exploit. Use containers or other sandboxing technologies.
    • Stay Updated: Apply ncnn updates promptly. Security patches are often included in new releases.