aisi

v1.1.10 suspicious
7.0
High Risk

AISI (AI Skill Infrastructure) is a python module, providing a modular framework environment for developing powerful AI-powered workflow systems in your projects.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits behaviors that raise concerns about potential malicious intent, particularly due to its high shell execution risk and network communication, although no direct evidence of malicious activities was found.

  • High shell execution risk
  • Potential network data exfiltration
Per-check LLM notes
  • Network: The package makes network calls which could be legitimate depending on its purpose, but without context, it raises suspicion for potential data exfiltration.
  • Shell: Executing commands via the shell can pose significant risks including arbitrary code execution, suggesting a high risk of malicious activity.
  • Obfuscation: The use of base64 decoding may indicate an attempt to obfuscate code, but it is also commonly used for legitimate purposes such as handling binary data in a text format.
  • Credentials: No patterns indicative of credential harvesting were detected.
  • Metadata: The repository not being found and the maintainer having only one package on PyPI suggest potential risk, but without clear malicious indicators.

📦 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 (1622 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

Could not retrieve contributor data from GitHub

  • GitHub API error: 404

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

  • try: r = requests.post(url, headers=headers, json=data) if r.status_co
  • l"] image_data = requests.get(image_url).content filename = f"grok_image_{int
⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • on image_bytes = base64.b64decode(image_base64) with open(path, "wb") as f:
⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

  • }' try: subprocess.run(command, shell=True) except KeyboardInterrupt:
  • subprocess.run(command, shell=True) except KeyboardInterrupt: sys.stderr
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: gmail.com

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 3.0

Repository not found (deleted or private)

  • Repository not found (deleted or private)
⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Irakli Gzirishvili" 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 aisi 
Develop a mini-app called 'SkillMaster' using the Python package 'aisi'. This app will serve as a personal skill development planner and mentor, leveraging AI to provide tailored advice and resources for learning new skills. Here’s a step-by-step guide on how to build it:

1. **Setup Project Environment**: Start by setting up a Python virtual environment and installing the 'aisi' package along with any other necessary dependencies.
2. **Define User Profiles**: Create a user profile system where users can input their current skill levels, interests, and career goals. Use 'aisi' to structure these profiles in a way that allows for dynamic updates based on user feedback.
3. **Skill Recommendation Engine**: Implement a recommendation engine that suggests skills for users to learn based on their profiles. Utilize 'aisi' to integrate machine learning models that can predict which skills would best suit each user.
4. **Learning Path Generator**: Develop a feature that generates personalized learning paths for each suggested skill. These paths should include recommended courses, books, and practice exercises. Use 'aisi' to automate the generation of these paths and ensure they are optimized for efficiency and effectiveness.
5. **Progress Tracking & Feedback System**: Incorporate a system that tracks user progress through their learning paths and provides feedback on performance. 'aisi' can help manage this data and offer insights into areas where users might need more focus.
6. **Community Features**: Integrate community elements such as forums, chat groups, or even virtual study sessions where users can connect and discuss their learning journeys. 'aisi' can facilitate these interactions by providing tools for managing user interactions and content.
7. **Integration with External Learning Resources**: Ensure that 'SkillMaster' can integrate with external learning platforms like Coursera, Udemy, or Khan Academy. 'aisi' can assist in automating the process of fetching relevant course information and integrating it into the app's learning paths.
8. **AI Mentor**: Implement an AI mentor feature that uses natural language processing (NLP) to interact with users, offering advice and answering questions related to their learning paths. 'aisi' can provide the infrastructure needed for building and deploying this NLP model.
9. **Analytics Dashboard**: Finally, create an analytics dashboard for both users and administrators to monitor overall engagement, popular skills, and trends in learning behavior. 'aisi' can help in visualizing this data effectively.

Throughout the development process, make sure to leverage 'aisi' to its fullest extent, taking advantage of its modular framework to enhance functionality, scalability, and user experience.