audiosentinel

v0.1.0 safe
3.0
Low Risk

Human vs AI audio detection via Shannon entropy features

🤖 AI Analysis

Final verdict: SAFE

The package AudioSentinel has a low risk score due to the absence of any risky behaviors such as network calls, shell executions, obfuscations, or credential harvesting. The metadata risk is slightly elevated due to its novelty and lack of a GitHub repository, but there are no clear signs of malicious intent.

  • No network calls detected
  • No shell execution patterns detected
  • No obfuscation or credential harvesting patterns detected
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require internet access.
  • Shell: No shell execution patterns detected, indicating the package likely does not execute system commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent related to code obfuscation.
  • Credentials: No credential harvesting patterns detected, suggesting no immediate risk of secret or credential theft.
  • Metadata: The package is new and lacks a GitHub repository, but there are no clear signs of malicious intent.

📦 Package Quality Overall: Low (2.0/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (3700 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
○ Low Type Annotations 1.0

No type annotations detected

  • No type annotations, py.typed marker, or stub files detected
○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

  • No GitHub repository linked — contributor count unavailable

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ 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

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • Author "Light" 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 audiosentinel 
Create a mini-application called 'AudioGuardian' which leverages the 'audiosentinel' package to distinguish between human-generated audio and AI-generated audio using Shannon entropy features. Your goal is to develop a user-friendly interface where users can upload audio files to be analyzed. AudioGuardian should output a report indicating whether the uploaded audio is likely to have been generated by a human or an AI. Additionally, include the following features:

1. User Interface: Design a simple web interface using Flask where users can upload their audio files.
2. Audio Analysis: Utilize the 'audiosentinel' package to compute Shannon entropy features from the uploaded audio file and classify it as either human-generated or AI-generated.
3. Result Display: After analysis, display a result page showing the classification outcome along with a confidence score.
4. Additional Features: Implement a feature to allow users to filter results based on different entropy thresholds, and provide a brief explanation of the entropy values and their significance in distinguishing human vs AI audio.

Ensure your application is well-documented and includes instructions for setting up and running the project locally.

💬 Discussion Feed

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