aiornot

v0.1.1 suspicious
6.0
Medium Risk

Python client for the AIORNOT API.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits significant shell execution risk and moderate network interaction, raising concerns about its security posture. However, there is no evidence of obfuscation or credential harvesting.

  • High shell risk due to potential for executing arbitrary code.
  • Moderate network risk from HTTP client usage.
Per-check LLM notes
  • Network: The use of an HTTP client suggests potential network activity which could be benign or malicious depending on the context.
  • Shell: Executing shell commands can be highly risky as it allows the package to run arbitrary code, potentially leading to system compromise.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer has a new or inactive account and lacks a proper author name, which could indicate low activity or oversight.

📦 Package Quality Overall: Medium (6.6/10)

✦ High Test Suite 9.0

Test suite present — 7 test file(s) found

  • 7 test file(s) detected (e.g. test_cli.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/aiornotinc/aiornot-python
  • Detailed PyPI description (12123 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

  • 109 type-annotated function signatures detected in source
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 3 unique contributor(s) across 33 commits in aiornotinc/aiornot-python
  • Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • lient as cc _SHARED_CLIENT = httpx.AsyncClient() class AsyncClient(BaseClient): def __init__(
✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

  • l) try: result = subprocess.run(cmd, check=True, capture_output=True, text=True) except
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: aiornot.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository aiornotinc/aiornot-python 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 aiornot 
Create a fully functional mini-application named 'AIOrNotChecker' that leverages the Python package 'aiornot' to determine if a given input string contains artificial intelligence-generated content. This application will serve as a tool for users to analyze text snippets and get insights into whether they were likely produced by AI or humans.

Step-by-Step Instructions:
1. Begin by installing the 'aiornot' package using pip.
2. Design a simple command-line interface (CLI) where users can input text snippets.
3. Implement a function that sends the input text to the AIORNOT API via 'aiornot' and receives a response indicating the likelihood of the text being AI-generated.
4. Display the results in a user-friendly format, including a confidence score.
5. Enhance the application by adding features such as saving the analysis results to a local file or database, allowing users to track their analyses over time.
6. Optionally, implement error handling to manage situations where the API is unavailable or returns unexpected responses.
7. Ensure the application has a help menu that explains how to use it and what each feature does.
8. Test the application thoroughly with various types of text inputs, including both human-written and AI-generated content, to ensure its accuracy and reliability.

Suggested Features:
- Ability to analyze multiple texts at once.
- Option to specify a threshold for the confidence score below which the result is marked as inconclusive.
- Integration with a logging mechanism to keep track of all analysis requests and results.
- Support for different languages, not just English.
- A graphical user interface (GUI) alternative to the CLI, making the tool more accessible to non-technical users.

How 'aiornot' Package is Utilized:
- The 'aiornot' package will be used to send HTTP requests to the AIORNOT API with the text input provided by the user.
- It will parse the JSON response from the API, extracting the relevant information about whether the text was likely generated by AI.
- The package simplifies the interaction with the API, abstracting away low-level details such as URL construction, headers, and data serialization.

💬 Discussion Feed

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