Cython

v3.2.5 suspicious
4.0
Medium Risk

The Cython compiler for writing C extensions in the Python language.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits some unusual behaviors such as potential shell execution risks and high levels of obfuscation, which could potentially be exploited for malicious purposes. However, there is no concrete evidence of harmful intent or credential harvesting.

  • Shell execution patterns detected
  • Significant obfuscation techniques observed
Per-check LLM notes
  • Network: No network calls detected, which is normal and expected.
  • Shell: Shell execution patterns detected may be related to building the package with C code, but could indicate potential risk if commands are used for unintended purposes.
  • Obfuscation: The code shows signs of obfuscation through dynamic imports and manipulation of the module namespace, which may be used to hide functionality or evade analysis.
  • Credentials: No clear evidence of credential harvesting is present in the provided code snippets.
  • Metadata: The maintainer has only one package, which may indicate a new or less active account, but no other suspicious activities are flagged.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 6.0

Found 3 obfuscation pattern(s)

  • pass' module_namespace = __import__(import_module).__dict__ if import_module else None cymeit_code = _CYM
  • le) else: __import__(module_name) module = sys.modules[module_name] s
  • __pyx_recursive_repr_guard = __import__('threading').local() __pyx_recursive_repr_guard.running = se
⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • ding libmymath.a") assert os.system("gcc -shared -fPIC -c mymath.c -o mymath.o") == 0 assert
  • -o mymath.o") == 0 assert os.system("ar rcs libmymath.a mymath.o") == 0 except: if not os.pa
  • ('.git')): assert os.system("git rev-parse --verify HEAD > .gitrev") == 0 sdist_
  • t subprocess returncode = subprocess.call(cmd, shell=shell) if returncode: sys.exit(retur
  • completed_process = subprocess.run( ["du", "-s", "-k", os.path.abspath(self.pat
  • ependencies. output = subprocess.run( [sys.executable, '-c', 'import builtins, json,
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: python.org

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository cython/cython appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Robert Bradshaw, Stefan Behnel, David Woods, Greg Ewing, et al." 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 Cython 
Create a high-performance number crunching utility using Python and the Cython package. This utility will allow users to perform complex mathematical operations on large datasets efficiently. The application should have the following core functionalities:

1. **Data Input Handling**: Users should be able to input a dataset through a simple command-line interface or a basic GUI. The data can be numerical values representing various mathematical sequences or series.
2. **Operation Selection**: Provide a menu-driven interface where users can select from a variety of mathematical operations such as calculating the Fibonacci sequence, finding prime numbers up to a certain limit, or computing factorials.
3. **Performance Optimization with Cython**: Utilize Cython to optimize the performance-critical parts of your application. Specifically, implement the mathematical operation algorithms in Cython to leverage its ability to compile Python code into C extensions, which can significantly speed up execution time compared to pure Python implementations.
4. **Result Display**: After performing the selected operation, display the results back to the user either in the console or through the GUI, depending on the input method chosen.
5. **Error Handling**: Implement robust error handling to manage cases where invalid inputs are provided or when an operation fails due to computational limitations.

In this project, focus on showcasing how Cython enhances the performance of your Python application. Document each step of the implementation process, including any challenges faced and solutions found during the optimization phase. Additionally, compare the performance of the Cython-optimized code with its pure Python counterpart to demonstrate the benefits of using Cython.