KapoorLabs-Lightning

v6.0.3 suspicious
7.0
High Risk

Lightning modules for KapoorLabs specific projects

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits elevated shell risk due to the execution of arbitrary commands, and moderate obfuscation which may conceal malicious intent. While there are no direct signs of credential theft or severe metadata issues, the combination of these factors raises suspicion.

  • High shell risk from executing arbitrary commands
  • Moderate obfuscation suggesting potential for hidden malicious activity
Per-check LLM notes
  • Network: The network calls appear to be related to ORCID token retrieval, which might be legitimate depending on the package's purpose.
  • Shell: Executing arbitrary commands via subprocess.run can pose significant risks, especially if input is not properly sanitized, indicating potential for exploitation.
  • Obfuscation: The observed patterns suggest code obfuscation which could potentially hide malicious activities, but without further context, it's unclear if it's benign or malicious.
  • Credentials: No clear evidence of credential harvesting patterns detected.
  • Metadata: No significant red flags except for non-HTTPS links to license pages.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

  • ri, }).encode() req = urllib.request.Request( ORCID_TOKEN_URL, data=data,
  • , ) try: with urllib.request.urlopen(req, timeout=15) as resp: return json.lo
⚠ Code Obfuscation score 10.0

Found 5 obfuscation pattern(s)

  • se, ) lightning_model.eval() lightning_model.to(device) val_loader = DataLoade
  • se ) lightning_model.eval() lightning_model.to(device) # Load val dataset
  • strict=False, ) model.eval() model.to(device) # Load data df = load_datafr
  • predict() already calls model.eval(), but be # defensive — accidentally training-mode d
  • corrupt scores. self.eval() temporal_raw, temporal_seg, timepoint, metadata =
⚠ Shell / Subprocess Execution score 6.0

Found 3 shell execution pattern(s)

  • t)) try: result = subprocess.run( cmd, capture_output=True,
  • tr(script_path)] result = subprocess.run(cmd, capture_output=True, text=True) if result.returnco
  • t = call(" ".join(joint_cmd), shell=True) # print result text if result ==
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: gmail.com

⚠ Suspicious Page Links score 10.0

Found 5 suspicious link(s) on the package page

  • Non-HTTPS external link: http://opensource.org/licenses/MIT
  • Non-HTTPS external link: http://opensource.org/licenses/BSD-3-Clause
  • Non-HTTPS external link: http://www.gnu.org/licenses/gpl-3.0.txt
  • Non-HTTPS external link: http://www.gnu.org/licenses/lgpl-3.0.txt
  • Non-HTTPS external link: http://www.apache.org/licenses/LICENSE-2.0
✓ Git Repository History

Repository Kapoorlabs-CAPED/KapoorLabs-Lightning appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Varun Kapoor, Mari Tolonen, Jakub Sedzinski" 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 KapoorLabs-Lightning 
Your task is to create a simple yet powerful weather forecasting mini-app using the KapoorLabs-Lightning package. This app will fetch real-time weather data from an API and use the package's Lightning modules to process and visualize the data in a user-friendly manner. Here’s a detailed breakdown of what your mini-app should accomplish:

1. **Data Fetching**: Integrate with a weather API such as OpenWeatherMap to fetch current weather conditions for a specified location. Ensure you handle API keys securely.
2. **Data Processing**: Use KapoorLabs-Lightning's Lightning modules to clean, preprocess, and transform the raw data into a format suitable for visualization. These modules might include functionalities like data normalization, anomaly detection, and feature engineering.
3. **Visualization**: Implement a simple GUI using a library like Tkinter or PyQt to display the processed weather data. Include visual elements like graphs, charts, and icons to represent temperature, humidity, wind speed, etc.
4. **User Interaction**: Allow users to input their location via a search bar or dropdown menu. Provide options to switch between different types of data views (e.g., hourly forecast vs. daily forecast).
5. **Error Handling**: Make sure to implement robust error handling for cases where the API call fails or returns unexpected data.
6. **Documentation**: Write clear and concise documentation explaining how to install and run the app, including any dependencies or setup steps required.

Suggested Features:
- Display current temperature, humidity, wind speed, and other relevant metrics.
- Offer historical weather data comparison for a week or month.
- Implement a feature to predict short-term weather trends based on past data.
- Provide alerts for extreme weather conditions like heavy rain or high temperatures.

The KapoorLabs-Lightning package will primarily be used for its advanced data processing capabilities, which will help in accurately interpreting the weather data received from the API. By leveraging this package, you'll ensure that the app not only looks good but also provides accurate and meaningful insights into the weather conditions.