alenia-zenith

v1.2.0 suspicious
6.0
Medium Risk

Startup optimization library for Python 3.10+: lazy imports, speculative pre-loading, and persistent module cache.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits high risks associated with shell execution and obfuscation techniques, which may be used to conceal malicious behavior. However, it does not appear to directly engage in credential harvesting or make network calls.

  • High shell execution risk due to subprocess usage
  • Significant obfuscation indicating possible hidden functionality
Per-check LLM notes
  • Network: No network calls were detected.
  • Shell: The use of subprocess.run to execute Python code from strings could indicate potential for executing arbitrary commands, suggesting elevated risk.
  • Obfuscation: The code shows signs of obfuscation through unconventional function calls and partial code snippets, which may indicate an attempt to hide functionality.
  • Credentials: No clear patterns indicative of credential harvesting were found in the provided code snippets.
  • Metadata: The maintainer has a new or inactive account and lacks a proper author name, which may indicate low credibility.

📦 Package Quality Overall: Low (4.8/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • 1 test file(s) detected (e.g. test_suite.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (10409 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Type checker (mypy / pyright / pytype) referenced in project
  • 37 type-annotated function signatures detected in source
○ Low Multiple Contributors 2.0

Single-author or unverifiable project

  • 1 unique contributor(s) across 19 commits in Kaia-Alenia/Zenith
  • Single author with few commits — possibly a personal or throwaway project

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 10.0

Found 5 obfuscation pattern(s)

  • er() for m in {mod_list}: __import__(m) print(time.perf_counter() - start) """ result = subproc
  • er() for m in {mod_list}: __import__(m) elapsed = time.perf_counter() - start import json print(jso
  • ) try: return __import__("json").loads(result.stdout.strip())["time"] except Exception:
  • er() for m in {mod_list}: __import__(m) print(time.perf_counter() - start) """ result = sub
  • tattr__(self, "_zenith_lock", __import__("threading").RLock()) def __getattribute__(self, name: str) -> Any:
⚠ Shell / Subprocess Execution score 8.0

Found 4 shell execution pattern(s)

  • r() - start) """ result = subprocess.run( [sys.executable, "-c", code], capture_outpu
  • elapsed}})) """ result = subprocess.run( [sys.executable, "-c", code], capture_outpu
  • AIL:{results}') """ res = subprocess.run( [sys.executable, "-c", thread_test_code], c
  • - start) """ result = subprocess.run( [sys.executable, "-c", code], captu
✓ 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

Repository Kaia-Alenia/Zenith 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 alenia-zenith 
Create a Python-based code editor utility named 'CodeSprint' that leverages the 'alenia-zenith' library to enhance startup performance and code execution speed. CodeSprint should include the following core functionalities:

1. **File Management**: Users should be able to open, edit, save, and close Python files within the editor.
2. **Syntax Highlighting**: Implement syntax highlighting specifically for Python code to improve readability.
3. **Autocomplete Suggestions**: Provide intelligent autocomplete suggestions based on the context of the code being written.
4. **Lazy Imports**: Utilize 'alenia-zenith' to implement lazy imports for any third-party libraries used within the editor, ensuring that these libraries are only loaded when necessary to speed up the initial loading time of the application.
5. **Speculative Pre-loading**: Integrate speculative pre-loading for frequently used libraries or modules to further reduce load times during code execution.
6. **Persistent Module Cache**: Employ a persistent module cache mechanism provided by 'alenia-zenith' to store previously loaded modules, thus avoiding redundant loading processes and enhancing overall performance.
7. **Performance Metrics**: Display real-time performance metrics such as startup time, module load times, and execution speed improvements achieved through the use of 'alenia-zenith'.

Your task is to design and develop CodeSprint from scratch, detailing each step of the process, including setting up the development environment, integrating 'alenia-zenith', and implementing the aforementioned features. Additionally, provide a brief explanation of how each feature utilizes 'alenia-zenith' to achieve its intended purpose.

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