MRFI

v2.1.0 suspicious
6.0
Medium Risk

A multi-resolution DNN fault injection tool

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risk due to obfuscated code and incomplete metadata from a potentially new maintainer. While direct threats like shell execution or network risks are not evident, the obfuscation raises concerns about hidden malicious functionalities.

  • Obfuscation risk of 7/10
  • Incomplete author information
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires internet access for its functionality.
  • Shell: No shell execution patterns detected, indicating no immediate risk of command injection or similar attacks.
  • Obfuscation: The code shows signs of obfuscation with unusual variable names and partial lines which may indicate an attempt to hide the true functionality.
  • Credentials: No clear patterns of credential harvesting are present in the provided snippets.
  • Metadata: The maintainer has a new or inactive account with incomplete author information, but no other suspicious activities are flagged.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 8.0

Found 4 obfuscation pattern(s)

  • el = MRFI(LeNet(trained=True).eval(), EasyConfig()) print(fi_model, fi_model.model) input_i
  • t18(pretrained = True).cuda().eval(), econfig) selector_cfg = fi_model.get_activation_config
  • t18(pretrained = True).cuda().eval(), econfig) ranges = get_activation_info(fi_model, make_tes
  • t18(pretrained = True).cuda().eval(), econfig) print('golden_Acc', Acc_golden(fi_model, ma
✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: outlook.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository fffasttime/MRFI appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with MRFI 
Develop a Fault Injection Analysis Tool using the MRFI Python package. This tool will serve as a diagnostic utility for deep neural networks (DNNs), enabling users to assess the robustness of their models against various faults at multiple resolutions. Here’s a detailed guide on how to build this application:

1. **Project Setup**: Begin by setting up your development environment. Ensure you have Python installed along with the MRFI package. You might also want to include other necessary libraries like TensorFlow or PyTorch for model creation and evaluation.

2. **Core Functionality**: Implement the main feature of your tool, which involves injecting faults into a DNN model at different resolution levels. Use MRFI's capabilities to simulate faults such as bit flips, transient errors, and stuck-at faults. Your tool should allow users to specify the type of fault, the layer of the network where the fault occurs, and the resolution level.

3. **User Interface**: Design a user-friendly interface (CLI or GUI) for interacting with the tool. Users should be able to input the path to their DNN model, select the type of fault injection, and view the results of the analysis. Consider adding options for saving and loading configurations.

4. **Result Visualization**: Integrate visualization tools to display the impact of faults on the model's performance. Graphical representations could include accuracy loss, precision-recall curves, and comparative analyses before and after fault injection.

5. **Advanced Features**:
   - **Customizable Fault Patterns**: Allow users to define custom fault patterns beyond the predefined ones provided by MRFI.
   - **Real-time Monitoring**: Implement real-time monitoring during the fault injection process to observe changes in model behavior dynamically.
   - **Report Generation**: Automate the generation of comprehensive reports detailing the fault injection experiments and their outcomes.

6. **Testing and Validation**: Rigorously test your application with various DNN architectures and datasets to ensure its reliability and effectiveness. Validate the results against known benchmarks or manually verified data points.

7. **Documentation and Deployment**: Write thorough documentation explaining how to use your tool, including setup instructions, configuration details, and usage examples. Prepare the application for deployment, ensuring it can run smoothly on different platforms and environments.

By following these steps, you'll create a powerful and versatile tool for assessing the fault tolerance of DNNs, leveraging the advanced features of the MRFI package.