arnio

v1.19.0 suspicious
5.0
Medium Risk

C++ accelerated data preparation for pandas and the Python data stack

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits signs of obfuscation and shell execution that require further scrutiny. While these could be benign, the combination with the incomplete metadata raises concerns about potential malicious intent.

  • High obfuscation risk
  • Potential shell execution
Per-check LLM notes
  • Network: No network calls detected, which is normal and not indicative of malicious activity.
  • Shell: Shell execution patterns observed may be benign depending on the package's purpose, but warrant further investigation to ensure there is no unauthorized command execution.
  • Obfuscation: The observed pattern suggests an attempt to bypass simple import checks or analysis tools, indicative of potential malicious obfuscation.
  • Credentials: No clear signs of credential harvesting were detected.
  • Metadata: The maintainer's author name is missing or very short, and they appear to be new or inactive, which raises some suspicion but not enough to conclusively label it as malicious.

📦 Package Quality Overall: Medium (7.4/10)

✦ High Test Suite 9.0

Test suite present — 9 test file(s) found

  • Test runner config found: pyproject.toml
  • Test runner config found: conftest.py
  • 9 test file(s) detected (e.g. conftest.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/im-anishraj/arnio#readme
  • Detailed PyPI description (69015 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

  • Classifier: Typing :: Typed
  • 235 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 39 unique contributor(s) across 100 commits in im-anishraj/arnio
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • IES: try: __import__(lib) results[lib] = (True, "Installed") exce
⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • -json", ] completed = subprocess.run( cmd, check=True, capture_output=Tru
  • ".join(cmd), flush=True) subprocess.run(cmd, check=True, cwd=cwd) def venv_python(env_dir: Path) -
  • _DRY_RUN"] = "1" result = subprocess.run( [sys.executable, str(benchmark_script)], cw
  • .py" try: subprocess.run( [sys.executable, str(generate_path)],
  • gs try: result = subprocess.run( cmd, env=env, capture_o
  • "--runs", "1"] result = subprocess.run( cmd, env={ **env, "
✓ 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 im-anishraj/arnio 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 arnio 
Create a data processing mini-app that leverages the 'arnio' package to accelerate data preparation tasks for pandas DataFrames. Your app should focus on handling large datasets efficiently, showcasing the speed and performance benefits of using 'arnio'. Here’s a step-by-step guide on what your application should accomplish:

1. **Project Setup**: Start by setting up a new Python environment and installing the necessary packages including 'arnio', pandas, and numpy.
2. **Data Generation**: Implement a feature to generate synthetic data that mimics real-world scenarios. This could include creating large datasets with mixed data types (integers, floats, strings).
3. **Data Preparation**: Utilize 'arnio' to preprocess the data. This includes operations like filtering, transforming, and aggregating data at high speeds. Demonstrate how 'arnio' accelerates these processes compared to standard pandas methods.
4. **Performance Benchmarking**: Integrate a simple benchmarking tool within the app to measure and compare the execution time of data preparation tasks using both 'arnio' and traditional pandas methods. This will highlight the performance gains achieved with 'arnio'.
5. **Visualization**: Use matplotlib or seaborn to visualize the performance benchmarks, showing the differences in execution times clearly.
6. **User Interface**: Develop a basic command-line interface (CLI) for users to interact with the app. Users should be able to specify the size of the dataset, choose which operations to perform, and view the results and performance metrics.
7. **Documentation and Reporting**: Finally, document the project thoroughly, explaining each step and the rationale behind using 'arnio'. Include a report summarizing the findings from the performance benchmarking tests.

This mini-app will serve as a practical example of how 'arnio' can enhance the efficiency of data preparation tasks in Python, making it particularly useful for developers working with large datasets.

💬 Discussion Feed

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