ai-microcore

v6.2.1 suspicious
5.0
Medium Risk

# Minimalistic Foundation for AI Applications

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risk due to the use of subprocess with shell=True and base64 decoding combined with eval, which could lead to code injection. However, there is no direct evidence of malicious intent.

  • Use of subprocess with shell=True
  • Base64 decoding and eval function
Per-check LLM notes
  • Network: No network calls detected, which is normal and expected.
  • Shell: Use of subprocess with shell=True can be risky if not sanitized properly, suggesting potential for command injection vulnerabilities.
  • Obfuscation: The use of base64 decoding and eval function suggests potential for code obfuscation or injection attacks.
  • Credentials: No clear signs of credential harvesting detected.
  • Metadata: The author's information is incomplete and they appear to be new or inactive, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Medium (6.0/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (14719 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

  • 260 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 5 unique contributor(s) across 100 commits in Nayjest/ai-microcore
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 10.0

Found 5 obfuscation pattern(s)

  • e.data: image_bytes = base64.b64decode(oai_img.b64_json) img = Image( image_byt
  • buf try: result = eval(program, namespace) # pylint: disable=eval-used if
  • try: module = __import__(module_name, fromlist=[class_name]) cls = getattr(module, class_name)
  • dule_name}") module = __import__(module_name, fromlist=[func_name]) fn = getattr(module, func_name) except (ImportE
  • %s\"", cache_name) return pickle.loads(storage.read(cache_name, binary=True)) def save_cache(cach
⚠ Shell / Subprocess Execution score 8.0

Found 4 shell execution pattern(s)

  • the output. """ with subprocess.Popen( argv, stdout=subprocess.PIPE, stder
  • "", "" try: with subprocess.Popen( cmd, stdout=subprocess.PIPE, stderr=subprocess.
  • ) try: out = subprocess.check_output(cmd, shell=True, text=True).strip() mu = [
  • subprocess.check_output(cmd, shell=True, text=True).strip() mu = [ _MemUsage(*
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: vitaliy.in>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository Nayjest/ai-microcore 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 ai-microcore 
Develop a simple yet powerful AI-driven task reminder application using the 'ai-microcore' Python package. This application will allow users to input tasks with specific due dates and receive notifications based on predefined conditions such as time of day or task priority. Here’s how you can structure your project:

1. **Setup**: Begin by installing the 'ai-microcore' package and setting up your development environment. Ensure you have Python installed and create a virtual environment for your project.
2. **Core Functionality**: Utilize 'ai-microcore' to handle the backend logic for task management and notification scheduling. Implement features to add, edit, delete, and view tasks.
3. **User Interface**: Design a user-friendly interface where users can easily interact with the application. Consider building a web-based UI using Flask or Django for simplicity.
4. **Notification System**: Integrate a notification system within the app. Use 'ai-microcore' to schedule notifications at specific times or intervals based on task deadlines.
5. **AI Enhancements**: Leverage 'ai-microcore' to introduce AI-driven enhancements like suggesting optimal times for task completion based on historical data and user behavior patterns.
6. **Testing & Deployment**: Thoroughly test your application to ensure all functionalities work as expected. Once ready, deploy your application using platforms like Heroku or AWS.

The goal is to demonstrate how 'ai-microcore' can streamline the development process while adding value through intelligent task management and personalized notifications.