🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows some signs of potential risk, particularly due to the detection of shell execution and incomplete metadata. These factors suggest that further scrutiny is warranted.

Shell risk detected
Incomplete maintainer metadata

Per-check LLM notes

Network: No network calls detected, which is normal and does not indicate risk.
Shell: Detection of shell execution may be legitimate if the package requires running external commands, but it could also indicate potential risks like executing arbitrary code, warranting further investigation.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
Credentials: No credential harvesting patterns detected, indicating low risk of secret or sensitive information theft.
Metadata: The maintainer's author name is missing or very short and seems to be a new or inactive account.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

p.join(sys.path) result = subprocess.run( [sys.executable, "-c", code], capture_outpu

✓ 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

Repository jmrplens/PyOctaveBand 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 PyOctaveBand

Develop a mini-application called 'SonicAnalyzer' using the Python package 'PyOctaveBand'. This tool will allow users to analyze audio files and visualize their frequency content across different octave bands. The application should include the following features:

1. **Audio File Input**: Users should be able to upload an audio file (e.g., .wav, .mp3). The application will support mono and stereo inputs.
2. **Frequency Analysis**: Utilize PyOctaveBand to perform octave-band and fractional octave-band filtering on the uploaded audio signal. Users should be able to select the desired number of octaves and the fraction of the octave they want to analyze.
3. **Visualization**: Provide a graphical interface where the user can see the frequency spectrum of the audio signal. The visualization should clearly show the amplitude distribution across different frequency bands.
4. **Export Results**: Allow users to export the analyzed data as a CSV file, which includes frequency band details and corresponding amplitudes.
5. **Customizable Settings**: Offer options for users to customize the analysis parameters such as sampling rate, window size, and overlap percentage.
6. **Interactive Interface**: Use a web-based framework like Flask or Django to create an interactive front-end for the application. The UI should be intuitive and easy to navigate.
7. **Documentation**: Include comprehensive documentation that explains how to use the SonicAnalyzer app, detailing each feature and parameter setting.

The core functionality of PyOctaveBand will be leveraged to process the audio signals into octave-band and fractional octave-band filtered outputs. These processed signals will then be visualized and analyzed to provide insights into the frequency content of the uploaded audio files.