Skip to content

Latest commit

 

History

History
30 lines (25 loc) · 4.33 KB

attack-surface.md

File metadata and controls

30 lines (25 loc) · 4.33 KB

Attack Surface Analysis for apache/mxnet

Attack Surface: Malicious Model Loading (Deserialization Vulnerabilities)

  • Description: Exploiting vulnerabilities during the process of loading and deserializing model files within MXNet. This attack surface arises from MXNet's model loading functionality itself.
  • MXNet Contribution: MXNet's core functionality includes parsing and deserializing model definition files (e.g., .json) and parameter files (e.g., .params). Vulnerabilities in MXNet's deserialization routines can be directly exploited.
  • Example: A crafted .json model file is designed to trigger a buffer overflow within MXNet's JSON parsing code during model loading. When an application using MXNet attempts to load this malicious model, it results in arbitrary code execution with the privileges of the application.
  • Impact: Arbitrary code execution, complete compromise of the system running the MXNet application, data breach, Denial of Service (DoS).
  • Risk Severity: Critical
  • Mitigation Strategies:
    • Strict Model Source Control: Only load models from highly trusted and internally managed sources. Implement rigorous verification processes for model files.
    • Input Validation (Limited Effectiveness): While challenging for complex model formats, attempt to implement basic validation of model file structure before loading.
    • Sandboxing and Isolation: Isolate the model loading and inference processes within sandboxed environments or containers with restricted permissions to limit the impact of successful exploits.
    • Regular MXNet Updates: Keep MXNet updated to the latest version to benefit from security patches addressing deserialization vulnerabilities.
    • Security Audits and Vulnerability Scanning: Conduct regular security audits specifically focusing on model loading procedures and utilize vulnerability scanning tools if available for MXNet model formats.

Attack Surface: Native Operator Vulnerabilities (Memory Corruption)

  • Description: Exploiting memory corruption vulnerabilities present in the native C++ implementations of MXNet's operators (layers, activation functions, etc.). This attack surface is inherent to the core computational components of MXNet.
  • MXNet Contribution: MXNet's performance-critical operations are implemented in native C++ code. Memory safety issues such as buffer overflows, use-after-free vulnerabilities, or integer overflows within these operator implementations directly expose the application to exploitation.
  • Example: A specific convolution operator in MXNet contains a buffer overflow vulnerability when processing input tensors with specific dimensions. An attacker crafts input data that triggers this overflow during model inference, allowing them to overwrite memory regions and potentially gain control to execute arbitrary code.
  • Impact: Arbitrary code execution, potential system compromise, information disclosure, Denial of Service (DoS).
  • Risk Severity: High
  • Mitigation Strategies:
    • Regular MXNet Updates: Prioritize keeping MXNet updated to the latest version. Security patches for operator vulnerabilities are often included in updates.
    • Input Sanitization and Validation: Implement input validation and sanitization for data fed into MXNet models to prevent unexpected or malicious inputs from triggering operator vulnerabilities. This is challenging for complex ML inputs but consider basic checks.
    • Resource Limits and Monitoring: Implement resource limits (e.g., memory limits) and monitoring to detect and mitigate potential DoS attempts exploiting operator vulnerabilities.
    • Consider Security Hardening: Explore security hardening techniques for the environment running MXNet, such as Address Space Layout Randomization (ASLR) and Data Execution Prevention (DEP), although these are general system-level mitigations.
    • Security Audits and Fuzzing (MXNet Developers/Community): Encourage and support security audits and fuzzing efforts on MXNet's native operator implementations to proactively identify and fix vulnerabilities. Users benefit from a more secure MXNet library.