arpakitlib

v1.9.51 suspicious
6.0
Medium Risk

arpakitlib

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows moderate risks due to network and shell usage, which could potentially lead to security vulnerabilities. The low credential and metadata risks slightly mitigate these concerns, but further investigation is warranted.

  • High shell risk
  • Moderate network and obfuscation risks
  • New or inactive maintainer account
Per-check LLM notes
  • Network: Network calls are common but should be reviewed for legitimacy and scope.
  • Shell: Use of shell=True can introduce security risks if command execution is not properly sanitized.
  • Obfuscation: The presence of base64 decoding suggests some level of obfuscation, but without more context it's hard to determine if it's malicious.
  • Credentials: No clear patterns indicative of credential harvesting have been detected.
  • Metadata: The maintainer has a new or inactive account and the repository lacks community engagement, which could indicate potential risk.

📦 Package Quality Overall: Low (4.4/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/ARPAKIT-Company/arpakitlib
  • Brief PyPI description (434 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 217 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 5.0

Limited contributor diversity

  • 1 unique contributor(s) across 59 commits in ARPAKIT-Company/arpakitlib
  • Single author but highly active (59 commits)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 4.5

Found 3 network call pattern(s)

  • , http_client=httpx.AsyncClient( timeout=httpx.Timeout(
  • try: async with aiohttp.ClientSession(connector=proxy_connector) as session: async
  • ) self.sync_client = paramiko.SSHClient() self.sync_client.set_missing_host_key_policy(param
⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • tes]: try: return base64.b64decode(base64_string) except Exception as e: if raise_f
⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

  • nCmdRes: subprocess_res = subprocess.run(command, shell=True, stderr=subprocess.PIPE, stdout=subproce
  • res = subprocess.run(command, shell=True, stderr=subprocess.PIPE, stdout=subprocess.PIPE, check=Fals
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: arpakit.com

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "arpakit_company" 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 arpakitlib 
Your task is to develop a mini-application called 'AudioVisualizer' which will take audio files as input and generate visual representations of their sound waves using the 'arpakitlib' library. This application will serve as both a tool for musicians and music enthusiasts to visualize their audio data and as a fun way to explore the properties of different types of audio files.

### Features:
1. **Audio File Input**: Allow users to upload any standard audio file format (MP3, WAV, etc.).
2. **Real-Time Visualization**: Display real-time visualizations of the sound waves as the audio plays. Users should be able to see the amplitude changes over time.
3. **Customization Options**: Provide options for users to customize the color scheme and waveform representation style (e.g., line graphs, bar charts).
4. **Export Functionality**: Allow users to save the generated visualizations as image files.
5. **Analysis Tools**: Include basic analysis tools such as frequency spectrum analysis, allowing users to view the distribution of frequencies within the audio.
6. **Interactive Controls**: Implement interactive controls to adjust playback speed, volume, and other relevant parameters.

### Utilizing 'arpakitlib':
- Use 'arpakitlib' to process and analyze the audio files. Specifically, utilize its functions for reading audio files, extracting key audio features like amplitude and frequency, and generating visual representations.
- Explore 'arpakitlib' documentation to understand how it handles different audio formats and ensure compatibility with a wide range of inputs.
- Leverage 'arpakitlib' for real-time processing capabilities, which will be crucial for the live visualization feature.
- Consider incorporating 'arpakitlib' examples and tutorials into your development process to optimize performance and accuracy of the visualizations.

💬 Discussion Feed

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