🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has a moderate risk score due to network and metadata concerns. While the use of requests.Session() is standard for making API calls, the presence of non-secure external links and limited author activity raise suspicions.

Network risk due to API calls
Metadata risk from non-secure external links

Per-check LLM notes

Network: The use of requests.Session() suggests the package makes network calls, which is common but should be reviewed to ensure it aligns with the package's intended functionality.
Shell: No shell execution patterns were detected.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package contains non-secure external links and has an author with limited activity, raising some suspicion but not conclusive evidence of malice.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

p('/') self.session = requests.Session() self.session.headers.update({ 'Authori

✓ Code Obfuscation

No obfuscation patterns detected

✓ 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: acrcloud.com>

⚠ Suspicious Page Links score 6.0

Found 3 suspicious link(s) on the package page

Non-HTTPS external link: http://stream.example.com/radio
Non-HTTPS external link: http://tv.example.com/stream
Non-HTTPS external link: http://stream.example.com

✓ Git Repository History

Repository acrcloud/acrcloud-cli 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 acrcloud-cli

Create a music recognition mini-app called 'SongSpotter' using the Python package 'acrcloud-cli'. This app will allow users to identify songs from audio clips they upload or record directly through their device's microphone. Here’s a detailed breakdown of what the app should do:

1. **User Interface**: Design a simple and intuitive graphical user interface (GUI) using a library like Tkinter or PyQt. The GUI should include options for uploading an audio file or recording audio directly from the user's device.
2. **Audio Handling**: Implement functionality to handle both pre-recorded audio files and live recordings. Ensure that the app can process these audio inputs efficiently and accurately.
3. **Integration with ACRCloud**: Use the 'acrcloud-cli' package to interact with the ACRCloud Console API. This includes setting up authentication credentials, making API calls to recognize the uploaded/recorded audio, and retrieving metadata about the identified song.
4. **Metadata Display**: Once a song is recognized, display relevant metadata such as the song title, artist name, album, release date, and genre on the GUI. Additionally, provide links to streaming services where the song can be listened to or purchased.
5. **Error Handling**: Implement robust error handling to manage cases where the API call fails or when no song is recognized from the audio input.
6. **User Preferences**: Allow users to save their favorite songs within the app and create playlists. Users should also be able to log in/out and manage their account preferences.
7. **Testing and Validation**: Test the app thoroughly under different conditions to ensure reliability and accuracy. Validate the functionality using a variety of audio samples, including those with varying quality and length.

By following these steps, you'll create a versatile and user-friendly music recognition tool that leverages the power of the 'acrcloud-cli' package.