aeroengine-fatigue-life

v1.0.0 suspicious
4.0
Medium Risk

ML-based fatigue life prediction for aeroengine alloys

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has some concerning aspects such as obfuscated code and lack of a public repository, indicating potential attempts to obscure its true functionality. However, there are no clear signs of malicious intent or direct harm.

  • Obfuscated variable names
  • No associated public repository
Per-check LLM notes
  • Network: No network calls detected, which is normal for a package focused on local computations like fatigue life analysis.
  • Shell: No shell executions detected, consistent with an application that performs calculations without system-level interactions.
  • Obfuscation: The code snippet shows obfuscated variable names which could indicate an attempt to hide the true purpose of the code, but it's not conclusively malicious without more context.
  • Credentials: No suspicious patterns related to credential harvesting were found in the provided snippet.
  • Metadata: The package appears to be new with limited activity and no associated repository, raising suspicion but lacking clear malicious indicators.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • oat32) self.model.eval() with torch.no_grad(): preds =
✓ 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: gmail.com

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 3.0

Repository not found (deleted or private)

  • Repository not found (deleted or private)
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • Author "RAVINDRANADH BOBBILI" 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 aeroengine-fatigue-life 
Create a Python-based web application that predicts the fatigue life of aeroengine alloys using machine learning models from the 'aeroengine-fatigue-life' package. This mini-app should allow users to input alloy material properties and operating conditions to receive predictions on how long an alloy will withstand cyclic stress before failing due to fatigue. The application should have a user-friendly interface, ideally built using Flask or Django for the backend and React or Vue.js for the frontend. Key features include:
1. A form where users can enter alloy composition, temperature range, and stress cycles.
2. Real-time validation of input data to ensure accuracy and prevent errors.
3. Integration with the 'aeroengine-fatigue-life' package to process the input and generate a fatigue life prediction.
4. Display of the predicted fatigue life in a clear, understandable format, including visual aids like charts or graphs if possible.
5. An explanation section that provides a brief overview of the significance of fatigue life predictions in aeroengine design and maintenance.
6. Optional advanced features could include saving user inputs and predictions, comparing multiple alloy types, and exporting results to a file.

The goal is to create a practical tool that can assist engineers in making informed decisions about aeroengine component durability and maintenance schedules.