aitraining

v0.0.54 suspicious
7.0
High Risk

Advanced Machine Learning Training Platform - IN DEVELOPMENT

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits high risks related to network and shell usage, suggesting potential vulnerabilities that could be exploited for malicious purposes. However, it lacks clear signs of direct malicious intent.

  • High network risk
  • High shell execution risk
Per-check LLM notes
  • Network: The package makes network calls to various endpoints, which could be legitimate but also indicative of data exfiltration or unauthorized access.
  • Shell: Use of os.system and subprocess.Popen with shell=True suggests potential for executing arbitrary commands, posing significant risk for malicious activities.
  • Obfuscation: The use of base64 encoding to decode image data is likely for legitimate purposes such as handling binary data in a text format.
  • Credentials: No suspicious patterns indicating credential harvesting were found.
  • Metadata: The package has no associated GitHub repository and the author information is sparse, indicating potential unreliability.

📦 Package Quality Overall: Low (4.2/10)

◈ 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 (11698 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

  • 149 type-annotated function signatures detected in source
○ 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 score 9.0

Found 6 network call pattern(s)

  • } try: response = requests.get( _api_url, headers=headers,
  • f.params.token}"} r = requests.post( ENDPOINTS_URL + self.username, json
  • try: response = requests.get(NGC_AUTH + "/token", headers=headers, params=querystring, ti
  • try: response = requests.post(NGC_API + url + "/jobs", headers=headers, json=payload, time
  • ": response = requests.post(url, headers=headers, json=payload, timeout=30)
  • response = requests.get(url, headers=headers) else:
⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • image_bytes = base64.b64decode(image_data) image = Image.open(io.BytesIO(im
⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • rok import ngrok os.system(f"fuser -n tcp -k {self.port}") authtoken = os.e
  • ig.yml" process = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.S
  • self._process = subprocess.Popen( cmd, stdout=subproc
  • """ subprocess.run(["osascript", "-e", script], check=False)
  • 2": process = subprocess.Popen( command, stdout=sub
  • se: process = subprocess.Popen( command, stdout=sub
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: monostate.ai>

✓ 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

  • 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 aitraining 
Create a mini-application called 'AI Tutor' using the Python package 'aitraining'. This application will serve as a personalized learning companion for students, capable of understanding their learning pace and providing customized study materials and quizzes. Here’s a step-by-step guide on how to develop this application:

1. **Setup Environment**: Begin by setting up your Python environment and installing the 'aitraining' package. Ensure you have all necessary dependencies installed.
2. **User Profile Creation**: Allow users to create profiles where they can specify their subject of interest, preferred learning style (e.g., visual, auditory), and current level of knowledge.
3. **Learning Path Generation**: Utilize 'aitraining' to generate a personalized learning path based on user profiles. This path should include recommended resources such as videos, articles, and interactive tutorials.
4. **Dynamic Quiz System**: Implement a dynamic quiz system that adjusts the difficulty of questions based on the user's performance. Use 'aitraining' to analyze answers and provide instant feedback along with explanations.
5. **Progress Tracking**: Integrate a feature to track user progress over time. Display metrics like time spent studying, number of topics mastered, and areas needing improvement.
6. **Feedback Loop**: Incorporate a feedback loop where users can rate the effectiveness of the learning materials and quizzes. Use this data to continuously improve the content through 'aitraining'.
7. **Social Sharing**: Add functionality allowing users to share their achievements and progress on social media platforms, fostering a sense of community among learners.
8. **Customization Options**: Provide options for customization such as adjusting notification preferences, choosing between light and dark themes, etc.
9. **Testing & Optimization**: Finally, test the application thoroughly to ensure all features work seamlessly. Optimize performance and fix any bugs identified during testing.

By following these steps, you'll create an engaging and effective tool that leverages advanced machine learning capabilities provided by 'aitraining' to enhance the learning experience.

💬 Discussion Feed

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