asking-machine

v0.0.1 suspicious
6.0
Medium Risk

A Machine that asks questions.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits high network and shell execution risks, indicating potential for unauthorized activities such as data exfiltration or system access. While there is no direct evidence of malicious intent, the package's behavior raises concerns about its security posture.

  • High network risk due to external API calls
  • High shell risk due to potential for executing commands
Per-check LLM notes
  • Network: Network calls to external APIs suggest potential data exfiltration or C2 communications.
  • Shell: Execution of shell commands indicates high risk of unauthorized system access or behavior.
  • Obfuscation: No obfuscation patterns detected in the provided code snippet.
  • Credentials: The usage of os.getenv for GitHub_TOKEN suggests an attempt to retrieve credentials, which could be legitimate but also indicates potential risk for credential harvesting if not properly secured.

📦 Package Quality Overall: Low (3.8/10)

✦ High Test Suite 9.0

Test suite present — 4 test file(s) found

  • 4 test file(s) detected (e.g. test_cli.py)
◈ Medium Documentation 5.0

Some documentation present

  • Brief PyPI description (748 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 2.0

Single-author or unverifiable project

  • 1 unique contributor(s) across 2 commits in asking-machine/asking-machine
  • Single author with few commits — possibly a personal or throwaway project

🔬 Heuristic Checks

⚠ Outbound Network Calls score 7.5

Found 5 network call pattern(s)

  • ing-Machine" } req = urllib.request.Request(url, headers=headers) try: with urllib.
  • aders) try: with urllib.request.urlopen(req, timeout=10) as response: return res
  • the Request object req = urllib.request.Request( f'{api_base}/chat/completions', dat
  • cute the request with urllib.request.urlopen(req, timeout=300) as response: response_
  • the Request object req = urllib.request.Request( f'{api_base}/models/{kwargs.get("model", co
✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

  • s.environ.copy() result = subprocess.run(cmd, input=input_text, text=True, capture_output=True, env=e
⚠ Credential Harvesting score 2.5

Found 1 credential access pattern(s)

  • default=os.getenv('GITHUB_TOKEN', 'no_token'), help="GitHub API tok
✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: aol.com>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 7.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
  • Very few commits: 2 total
  • Single contributor with only 2 commit(s) — possibly throwaway account
⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • 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 asking-machine 
Create a personalized health tracker app using Python's 'asking-machine' package. This app will help users monitor their daily habits and health metrics through a series of interactive questions asked by the 'asking-machine'. Here's a step-by-step guide on how to build this app:

1. **Setup Your Environment**: Ensure you have Python installed. Install the 'asking-machine' package using pip.
2. **Design the User Interface**: Since this is a command-line application, design a simple and user-friendly interface where users can interact with your app easily.
3. **Define Health Metrics**: Decide on the health metrics you want to track. These could include sleep duration, water intake, exercise minutes, etc.
4. **Integrate 'Asking-Machine'**: Use 'asking-machine' to create a questionnaire for each health metric. Each question should aim to gather accurate data from the user.
5. **Data Storage**: Implement a system to store the collected data. This could be as simple as writing to a CSV file or more complex like using a database.
6. **Generate Reports**: After collecting data over a period, generate reports that summarize the user's health habits. This can include trends, averages, and comparisons.
7. **Optional Features**:
   - Allow users to set goals for each metric.
   - Provide reminders for the user to input their daily data.
   - Offer tips or suggestions based on the user's responses.
8. **Testing and Feedback**: Test your app thoroughly and gather feedback from potential users to improve the app's functionality and usability.

This project will not only enhance your understanding of Python packages but also provide a practical tool for monitoring personal health.

💬 Discussion Feed

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