b2aiprep

v3.3.3 suspicious
4.0
Medium Risk

A small package to generate features from acoustic

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows moderate risk due to potential shell execution and incomplete metadata, though no clear signs of malicious activity are present.

  • moderate shell execution risk
  • incomplete author and maintainer information
Per-check LLM notes
  • Network: The network patterns detected seem to be related to checking URLs and possibly downloading content, which could be legitimate for a preparatory tool but should be reviewed for destinations and purposes.
  • Shell: The shell execution patterns include git commands which might be used for version control operations, but direct execution of arbitrary commands could pose a risk if not properly sanitized or controlled.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
  • Credentials: No credential harvesting patterns detected, indicating low risk of malicious credential theft.
  • Metadata: The author information is incomplete and the maintainer seems new or inactive, which raises some concern but does not definitively indicate malicious intent.

📦 Package Quality Overall: Medium (6.2/10)

✦ High Test Suite 9.0

Test suite present — 15 test file(s) found

  • 15 test file(s) detected (e.g. test_bids.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (1879 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 200 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 6 unique contributor(s) across 100 commits in sensein/b2aiprep
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

⚠ Outbound Network Calls score 6.0

Found 4 network call pattern(s)

  • " try: response = requests.get(url) return response.status_code == 200 except r
  • " try: response = requests.get(url, timeout=10) if response.status_code != 200:
  • session = http_session or requests.Session() for url in missing: try:
  • y data http_session = requests.Session() # Shared cache for choice definitions across all s
✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • p() try: result = subprocess.run( ["git", "-C", str(submodule_root), "rev-parse",
  • staged files result = subprocess.run(['git', 'diff', '--name-only'],
  • pecific file result = subprocess.run(['git', 'checkout', '--', file_path],
  • the CLI command result = subprocess.run(command, capture_output=True, text=True) # Check if the
  • ard", "--help"] result = subprocess.run(command, capture_output=True, text=True) assert result.r
  • , ] result = subprocess.run(command, capture_output=True, text=True) assert resu
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: mit.edu>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository sensein/b2aiprep 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 b2aiprep 
Create a mini-application named 'AudioFeatureGenerator' using Python and the package 'b2aiprep'. This application will serve as a tool for audio enthusiasts and researchers to easily extract meaningful features from raw audio files, which can then be used for further analysis or machine learning tasks. Here’s a step-by-step guide on how to develop this application:

1. **Project Setup**: Initialize your Python environment with the necessary packages including 'b2aiprep', 'numpy', 'pandas', and 'matplotlib'. Make sure to install 'b2aiprep' if it's not available in PyPI.
2. **User Interface Design**: Design a simple command-line interface (CLI) where users can input the path to their audio file. Optionally, provide a GUI using Tkinter or PyQt for a more interactive experience.
3. **Audio File Handling**: Implement functionality within the application to read various types of audio files (e.g., .wav, .mp3). Use 'b2aiprep' to preprocess these files and convert them into a format suitable for feature extraction.
4. **Feature Extraction**: Utilize 'b2aiprep' to generate a set of acoustic features from the preprocessed audio data. Ensure you document each feature clearly (e.g., spectral centroid, zero-crossing rate).
5. **Visualization**: Provide visual representations of the extracted features using matplotlib. For example, plot the spectral centroid over time or visualize the Mel-frequency cepstral coefficients (MFCCs).
6. **Output Options**: Allow users to export the extracted features either as a CSV file or directly into a pandas DataFrame for easy integration into other Python projects.
7. **Advanced Features** (Optional): Consider adding advanced functionalities such as real-time feature extraction, support for multiple audio files at once, or even basic machine learning models trained on the extracted features.
8. **Documentation & Testing**: Write comprehensive documentation explaining how to use the application and its features. Include a series of test cases to ensure the reliability and accuracy of the feature extraction process.

By following these steps, you'll create a valuable tool for anyone working with audio data, making complex feature extraction accessible and straightforward.

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